Robot Skill Learning

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21 papers in the last four weeks, up 17% on the four weeks before. 0.2% of all new papers.

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Latest papers 261

Mar 9, 2026cs.RO

See and Switch: Vision-Based Branching for Interactive Robot-Skill Programming

Programming by demonstration (PbD) makes robot programming accessible to non-experts, but scaling it to real-world variability remains a challenge for current teaching frameworks, especially when a robot must select suitable task variants online from visual input. We present See & Switch, an interactive teaching-and-execution framework that represents tasks as graphs of skill parts connected by decision states, enabling conditional branching during replay. Its vision-based Switcher uses eye-in-hand images to select the appropriate successor skill part and detect novel situations that require new demonstrations. The framework supports recovery demonstrations during execution through kinesthetic teaching, joystick control, and hand gestures. We evaluate See & Switch on three dexterous manipulation tasks with 8 novice users, collecting approx. 900 real-robot execution rollouts. To isolate visual decision performance from timing errors during decision states, we evaluate the Switcher offline using user-gated decision state windows. In the evaluation within the decision state windows, the method achieves up to 90.6% branch-selection accuracy and detects anomalies with >90% accuracy in 47 of 79 decision states, demonstrating reliable switching based on visual input for conditional robot-skill programming. We provide all code and experiment data at http://imitrob.ciirc.cvut.cz/publications/seeandswitch.

Petr Vanc, Jan Kristof Behrens, Václav Hlaváč +1

imitrob.ciirc.cvut.cz/publications/seeandswitch

Robot Skill Learning Robotic Manipulation Learning from Demonstration Robot Imitation Learning Vision-Based Robot Control Robot Learning

Mar 5, 2026cs.RO

Task Parameter Extrapolation via Learning Inverse Tasks from Forward Demonstrations

Generalizing skill policies to novel conditions remains a key challenge in robot learning. Imitation learning methods, while data-efficient, are largely confined to the training region and consistently fail on input data outside it, leading to unpredictable policy failures. Alternatively, transfer learning approaches offer methods for trajectory generation robust to both changes in environment and tasks, but they remain data-hungry and lack accuracy in zero-shot generalization. We address these challenges in the context of task inversion learning and propose a novel joint learning approach to achieve accurate and efficient knowledge transfer. Our method constructs a common representation of the forward and inverse tasks, and leverages auxiliary forward demonstrations from novel configurations to successfully execute the corresponding inverse tasks, without any direct supervision. We demonstrate the extrapolation capabilities of our framework through ablation studies and experiments in simulated and real-world environments that require complex manipulation skills with a diverse set of objects and tools, where we outperform diffusion-based and multimodal VAE alternatives.

Serdar Bahar, Fatih Dogangun, Matteo Saveriano +2

Robot Skill Learning Generalization in Robotic Manipulation Robotic Manipulation Robot Imitation Learning Robot Policy Generalization

Mar 4, 2026cs.RO

From Local Corrections to Generalized Skills: Improving Neuro-Symbolic Policies with MEMO

Recent works use a neuro-symbolic framework for general manipulation policies. The advantage of this framework is that -- by applying off-the-shelf vision and language models -- the robot can break complex tasks down into semantic subtasks. However, the fundamental bottleneck is that the robot needs skills to ground these subtasks into embodied motions. Skills can take many forms (e.g., trajectory snippets, motion primitives, coded functions), but regardless of their form skills act as a constraint. The high-level policy can only ground its language reasoning through the available skills; if the robot cannot generate the right skill for the current task, its policy will fail. We propose to address this limitation -- and dynamically expand the robot's skills -- by leveraging user feedback. When a robot fails, humans can intuitively explain what went wrong (e.g., ``no, go higher''). While a simple approach is to recall this exact text the next time the robot faces a similar situation, we hypothesize that by collecting, clustering, and re-phrasing natural language corrections across multiple users and tasks, we can synthesize more general text guidance and coded skill templates. Applying this hypothesis we develop Memory Enhanced Manipulation (MEMO). MEMO builds and maintains a retrieval-augmented skillbook gathered from human feedback and task successes. At run time, MEMO retrieves relevant text and code from this skillbook, enabling the robot's policy to generate new skills while reasoning over multi-task human feedback. Our experiments demonstrate that using MEMO to aggregate local feedback into general skill templates enables generalization to novel tasks where existing baselines fall short. See supplemental material here: https://collab.me.vt.edu/memo

Benjamin A. Christie, Yinlong Dai, Mohammad Bararjanianbahnamiri +2

Collab, Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, USA. · TEA Lab, Dept. of Mechanical Engineering, Virginia Tech, Blacksburg, USA.

Robot Skill Learning Language-Conditioned Robot Manipulation Generalization in Robotic Manipulation Robot Policy Generalization Neurosymbolic Learning Memory-Augmented Agents+1

Feb 18, 2026cs.RO

SimToolReal: An Object-Centric Policy for Zero-Shot Dexterous Tool Manipulation

The ability to manipulate tools significantly expands the set of tasks a robot can perform. Yet, tool manipulation represents a challenging class of dexterity, requiring grasping thin objects, in-hand object rotations, and forceful interactions. Since collecting teleoperation data for these behaviors is challenging, sim-to-real reinforcement learning (RL) is a promising alternative. However, prior approaches typically require substantial engineering effort to model objects and tune reward functions for each task. In this work, we propose SimToolReal, taking a step towards generalizing sim-to-real RL policies for tool manipulation. Instead of focusing on a single object and task, we procedurally generate a large variety of tool-like object primitives in simulation and train a single RL policy with the universal goal of manipulating each object to random goal poses. This approach enables SimToolReal to perform general dexterous tool manipulation at test-time without any object or task-specific training. We demonstrate that SimToolReal outperforms prior retargeting and fixed-grasp methods by 37% while matching the performance of specialist RL policies trained on specific target objects and tasks. Finally, we show that SimToolReal generalizes across a diverse set of everyday tools, achieving strong zero-shot performance over 120 real-world rollouts spanning 24 tasks, 12 object instances, and 6 tool categories.

Kushal Kedia, Tyler Ga Wei Lum, Jeannette Bohg +1

Cornell University · Stanford University

Zero-Shot Robotic Manipulation Robot Skill Learning Sim-to-Real Transfer Generalization in Robotic Manipulation Robotic Tool Use Robotic Manipulation+1

Jan 27, 2026cs.RO

Sim-and-Human Co-training for Data-Efficient and Scene-Generalizable Bimanual Manipulation

Real-robot demonstrations are prohibitively expensive, while simulation data and real-world human demonstrations are both scalable but each leaves a distinct gap: simulation suffers from a sim-to-real visual gap, and human data suffers from a human-to-robot embodiment gap. In this work, we identify a natural yet underexplored complementarity between these sources: simulation contributes robot-valid actions absent in human data, while human data provides real-world observations that simulation struggles to render. Building on this insight, we present SimHum, a co-training recipe that extracts kinematic priors from simulation and visual priors from human observations, then fine-tunes on a small real-robot dataset. SimHum exhibits strong scene-generalizable and data-efficient capabilities. With only 80 real-robot episodes per task, it achieves 62.5% success on held-out OOD scenes across four bimanual tabletop tasks, 53.7% higher than Real only in absolute success rate. Moreover, in a controlled data-collection study with matched collection time, SimHum improves over the best single-source pre-training baseline by 35.0% in absolute success rate. Project page: https://kaipengfang.github.io/sim-and-human/

Kaipeng Fang, Weiqing Liang, Yuyang Li +5

University of Electronic Science and Technology of China · Southwest Jiaotong University · Tongji University +1

Robot Skill Learning Bimanual Robotic Manipulation Sim-to-Real Transfer Generalization in Robotic Manipulation Cross-Embodiment Robot Learning Data-Efficient Robot Learning+1

Dec 30, 2025cs.RO

World In Your Hands: A Large-Scale and Open-Source Ecosystem for Learning Human-Centric Manipulation in the Wild

We introduce World In Your Hands (WIYH), a large-scale open-source ecosystem comprising over 1,000 hours of human manipulation data collected in-the-wild with millimeter-scale motion accuracy. Specifically, WIYH includes (1) the Oracle Suite, a wearable data collection kit with an auto-labeling pipeline for accurate motion capture; (2) the WIYH Dataset, featuring over 1,000 hours of multimodal manipulation data across hundreds of skills in diverse real-world scenarios; and (3) extensive annotations and benchmarks supporting tasks from perception to action. Furthermore, experiments based on the WIYH ecosystem show that integrating WIYH's human-centric data improves robotic manipulation success rates from 8% to 60% in cluttered scenes. World In Your Hands provides a foundation for advancing human-centric data collection and cross-embodiment policy learning. All data and hardware design will be open-source.

Yupeng Zheng, Jichao Peng, Weize Li +22

TARS Robotics

Robot Skill Learning Robotic Data Collection Cross-Embodiment Robot Learning Robotic Manipulation Robot Manipulation Benchmarks Robot Learning+1

Dec 20, 2025cs.RO

Learning Semantic Atomic Skills for Multi-Task Robotic Manipulation

Scaling imitation learning to diverse multi-task robot manipulation remains challenging due to suboptimal demonstrations, behavioral multi-modality, and destructive interference across tasks. While skill-based methods offer a promising direction by decomposing behaviors into reusable abstractions, existing approaches often learn skills that are either biased toward linguistic structure or lack semantic alignment across tasks, limiting generalization. In this work, we propose AtomSkill, a novel framework that learns a semantically aligned Atomic Skill Space from demonstrations and enables robust long-horizon execution through keypose imagination. Our method introduces: (1) semantic contrastive skill alignment, which partitions demonstrations into variable-length atomic skills and employs a contrastive objective to jointly enforce semantic consistency and temporal coherence, yielding a compact and reusable skill library; and (2) action decoding with keypose imagining, where the policy predicts both a skill's terminal keypose and immediate actions, thereby supporting progress-aware skill transitions. During inference, an atomic skill diffusion sampler generates plausible skill sequences, while predicted keyposes autonomously trigger smooth skill chaining. Extensive experiments in simulation and real-world settings show that AtomSkill consistently outperforms state-of-the-art imitation learning and skill-based baselines. Project page: https://atom-skill.github.io.

Yihang Zhu, Weiqing Wang, Shijie Wu +2

ShanghaiTech University, Shanghai, China · InstAdapt

Long-Horizon Robotic Manipulation Robot Skill Learning Generalization in Robotic Manipulation Multi-Task Robotic Manipulation Robotic Manipulation Robot Imitation Learning+1

Dec 17, 2025cs.RO

ISS Policy : Scalable Diffusion Policy with Implicit Scene Supervision

Vision-based imitation learning has enabled impressive robotic manipulation skills, but action imitation alone provides limited supervision of the geometric consequences of robot behavior. To address this limitation, we introduce Implicit Scene Supervision (ISS) Policy, a 3D visuomotor diffusion policy with a DiT backbone that predicts continuous action sequences from point-cloud observations. ISS augments action diffusion with a supervised robot motion predictor that maps generated actions and robot-state context to end-effector motion, and then uses the predicted motion together with gripper intent to forecast future point-cloud representations. By explicitly modeling the intermediate transition from action to robot motion, ISS encourages the policy to capture how its actions affect the surrounding 3D scene. We further introduce asymmetric gradient routing to separate direct motion regression from scene-level policy supervision, together with a change-balanced objective that accounts for variations in scene-change magnitude. These auxiliary objectives provide dynamics-aware geometric supervision using only expert demonstrations, without requiring additional annotations or auxiliary modules at inference time. ISS Policy achieves state-of-the-art performance on single-arm manipulation tasks in MetaWorld and dexterous manipulation tasks in Adroit, while real-world dual-arm experiments further demonstrate its effectiveness on physical robotic manipulation. The resulting framework preserves the scalable DiT backbone and standard diffusion-policy control interface. Code and videos will be released.

Wenlong Xia, Jinhao Zhang, Ce Zhang +6

[cs.RO]

Robot Skill Learning Visuomotor Policy Learning Diffusion Policy Robotic Manipulation Robot Imitation Learning

Dec 11, 2025cs.RO

Learning Category-level Last-meter Navigation from RGB Demonstrations of a Single-instance

Achieving precise positioning of the mobile manipulator's base is essential for successful manipulation actions that follow. Most of the RGB-based navigation systems only guarantee coarse, meter-level accuracy, making them less suitable for the precise positioning phase of mobile manipulation. This gap prevents manipulation policies from operating within the distribution of their training demonstrations, resulting in frequent execution failures. We address this gap by introducing an object-centric imitation learning framework for last-meter navigation, enabling a quadruped mobile manipulator robot to achieve manipulation-ready positioning using only RGB observations from its onboard cameras. Our method conditions the navigation policy on three inputs: goal images, multi-view RGB observations from the onboard cameras, and a text prompt specifying the target object. A language-driven segmentation module and a spatial score-matrix decoder then supply explicit object grounding and relative pose reasoning. Using real-world data from a single object instance within a category, the system generalizes to unseen object instances across diverse environments with challenging lighting and background conditions. To comprehensively evaluate this, we introduce two metrics: an edge-alignment metric, which uses ground truth orientation, and an object-alignment metric, which evaluates how well the robot visually faces the target. Under these metrics, our policy achieves 74.58% success in edge-alignment and 89.42% success in object-alignment when positioning relative to unseen target objects. These results show that precise last-meter navigation can be achieved at a category-level without depth, LiDAR, or map priors, enabling a scalable pathway toward unified mobile manipulation. Project page: https://rpm-lab-umn.github.io/category-level-last-meter-nav/

Tzu-Hsien Lee, Fidan Mahmudova, Karthik Desingh

University of Minnesota, Twin Cities

Robot Skill Learning Robot Navigation Robot Imitation Learning Object-Goal Navigation

Nov 4, 2025cs.RO

XR-1: Towards Versatile Vision-Language-Action Models via Learning Unified Vision-Motion Representations

Recent progress in large-scale robotic datasets and vision-language models (VLMs) has advanced research on vision-language-action (VLA) models. However, existing VLA models still face two fundamental challenges: (i) producing precise low-level actions from high-dimensional observations, (ii) bridging domain gaps across heterogeneous data sources, including diverse robot embodiments and human demonstrations. Existing methods often encode latent variables from either visual dynamics or robotic actions to guide policy learning, but they fail to fully exploit the complementary multi-modal knowledge present in large-scale, heterogeneous datasets. In this work, we present X Robotic Model 1 (XR-1), a novel framework for versatile and scalable VLA learning across diverse robots, tasks, and environments. XR-1 introduces the \emph{Unified Vision-Motion Codes (UVMC)}, a discrete latent representation learned via a dual-branch VQ-VAE that jointly encodes visual dynamics and robotic motion. UVMC addresses these challenges by (i) serving as an intermediate representation between the observations and actions, and (ii) aligning multimodal dynamic information from heterogeneous data sources to capture complementary knowledge. To effectively exploit UVMC, we propose a three-stage training paradigm: (i) self-supervised UVMC learning, (ii) UVMC-guided pretraining on large-scale cross-embodiment robotic datasets, and (iii) task-specific post-training. We validate XR-1 through extensive real-world experiments with more than 14,000 rollouts on six different robot embodiments, spanning over 120 diverse manipulation tasks. XR-1 consistently outperforms state-of-the-art baselines such as

π_{0.5}

,

π_0

, RDT, UniVLA, and GR00T-N1.5 while demonstrating strong generalization to novel objects, background variations, distractors, and illumination changes. Our project is at https://xr-1-vla.github.io/.

Shichao Fan, Kun Wu, Zhengping Che +12

Beijing Innovation Center of Humanoid Robotics, Beijing, China · State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University, Beijing, China · State Key Laboratory of Virtual Reality Technology and Systems, SCSE, Beihang University, Beijing, China +1

Representation Learning Robot Skill Learning Cross-Embodiment Robot Learning Robotic Manipulation Vision-Language-Action Models

Sep 23, 2025cs.RO

Robotic Ultra-Long-Horizon Manipulation Skills via Human-guided Lifelong Code Generation

Large language models (LLMs) can translate natural-language instructions for robotic manipulation into executable code, but ambiguity, noisy generations, and limited context windows make ultra-long-horizon tasks unreliable. Closed-loop approaches that rely only on LLM feedback also struggle because LLMs have limited robotic reasoning, even when task errors are obvious to humans. Feedback is often stored in representations that generalize poorly to unseen tasks and can cause catastrophic forgetting as new corrections accumulate. We propose LYRA, a human-guided lifelong skill learning and code generation framework that distills human feedback into modular, reusable skills and incrementally extends their functionality across successive interactions while preserving previously learned behavior. External memory stores learned skills and execution examples; retrieval-augmented generation selects relevant knowledge, while user hints guide reuse when retrieval is insufficient, supporting ultra-long-horizon execution. Experiments on Ravens, Franka Kitchen, LIBERO-long, MetaWorld, and real-world tasks show a 0.93 success rate, up to 27% higher than baselines, and a 42% improvement in correction efficiency. LYRA also robustly solves ``build a house'', which requires planning over 20 primitives.

Yuan Meng, Zhenguo Sun, Max Fest +4

School of Computation, Information and Technology Technical University of Munich Munich, Germany · Beijing Academy of Artificial Intelligence (BAAI) Beijing, China · The State Key Laboratory for Novel Software Technology Nanjing University Suzhou, China

Long-Horizon Robotic Manipulation Robot Skill Learning Robotic Manipulation Continual Robot Learning Human-in-the-Loop AI

Sep 22, 2025cs.RO

GeCCo -- a Generalist Contact-Conditioned Policy for Loco-Manipulation Skills on Legged Robots

Most modern approaches to quadruped locomotion focus on using Deep Reinforcement Learning (DRL) to learn policies from scratch, in an end-to-end manner. Such methods often fail to scale, as every new problem or application requires time-consuming and iterative reward definition and tuning. We present Generalist Contact-Conditioned Policy (GeCCo) --- a low-level policy trained with Deep Reinforcement Learning that is capable of tracking arbitrary contact points with a quadruped robot. We shift from task-specific end-to-end learning to a modular hierarchy in which a single planner-agnostic, contact-conditioned tracking policy serves as a robust interface between planning and control. By maintaining stable contact execution under dynamic uncertainty and planner mismatch, the policy enables high-level planners (e.g., handcrafted, learned, or optimization-based) to be composed and swapped without retraining. We demonstrate the scalability and robustness of our method by evaluating on a wide range of locomotion and manipulation tasks in a common framework and under a single generalist policy. These include a variety of gaits, traversing complex terrains (e.g., stairs and slopes) as well as previously unseen stepping-stones and narrow beams, and interacting with objects (e.g., pressing a button, pushing a barrel). Our framework acquires new behaviors more efficiently, simply by combining a task-specific high-level contact planner and the pre-trained generalist policy. Project website: https://vassil-atn.github.io/gecco.github.io

Vassil Atanassov, Wanming Yu, Siddhant Gangapurwala +2

Dynamic Robot Systems Group, Oxford Robotics Institute, University of Oxford · Sony AI · Dyson Institute of Engineering and Technology

Robotic Control Legged Robotics Robot Policy Learning Robot Skill Learning Robotic Manipulation RL for Legged Locomotion+1

Sep 21, 2025cs.RO

Imagine2Act: Leveraging Object-Action Motion Consistency from Imagined Goals for Robotic Manipulation

Relational object rearrangement (ROR) tasks (e.g., insert flower to vase) require a robot to manipulate objects with precise semantic and geometric reasoning. Existing approaches either rely on pre-collected demonstrations that struggle to capture complex geometric constraints or generate goal-state observations to capture semantic and geometric knowledge, but fail to explicitly couple object transformation with action prediction, resulting in errors due to generative noise. To address these limitations, we propose Imagine2Act, a 3D imitation-learning framework that incorporates semantic and geometric constraints of objects into policy learning to tackle high-precision manipulation tasks. We first generate imagined goal images conditioned on language instructions and reconstruct corresponding 3D point clouds to provide robust semantic and geometric priors. These imagined goal point clouds serve as additional inputs to the policy model, while an object-action consistency strategy with soft pose supervision explicitly aligns predicted end-effector motion with generated object transformation. This design enables Imagine2Act to reason about semantic and geometric relationships between objects and predict accurate actions across diverse tasks. Experiments in both simulation and the real world demonstrate that Imagine2Act outperforms previous state-of-the-art policies. More visualizations can be found at https://sites.google.com/view/imagine2act.

Liang Heng, Jiadong Xu, Yiwen Wang +6

CFCS, School of Computer Science, Peking University · PrimeBot · AGIBOT

Robot Skill Learning Language-Conditioned Robot Manipulation Robotic Manipulation Robot Imitation Learning

Aug 28, 2025cs.RO

Physics-Guided Residual Reinforcement Learning for Humanoid Narrow-Path Traversal

Traversing narrow paths is challenging for humanoid robots due to the sparse and safety-critical footholds required. Purely template-based or end-to-end reinforcement learning-based methods suffer from such harsh terrains. This paper proposes a two stage training framework for such narrow path traversing tasks, coupling a template-based foothold planner with a low-level foothold tracker from Stage-I training and a lightweight perception aided foothold modifier from Stage-II training. With the curriculum setup from flat ground to narrow paths across stages, the resulted controller in turn learns to robustly track and safely modify foothold targets to ensure precise foot placement over narrow paths. This framework preserves the interpretability from the physics-based template and takes advantage of the generalization capability from reinforcement learning, resulting in easy sim-to-real transfer. The learned policies outperform purely template-based or reinforcement learning-based baselines in terms of success rate, centerline adherence and safety margins. Validation on a Unitree G1 humanoid robot yields successful traversal of a 0.2m wide and 3m long beam for 20 trials without any failure.

Tianchen Huang, Sisheng Chen, Wei Zhou +8

Institute of Humanoid Robots, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, Hefei, Anhui 230026, China

Humanoid Robot Locomotion Robot Skill Learning Robot Locomotion RL for Legged Locomotion Residual Policy Learning

Aug 12, 2025cs.RO

Rational Inverse Reasoning: Few-Shot Imitation by Inferring Intent through Planning

Humans can learn a new manipulation task from one or two demonstrations and then perform it in a new room, with new objects, under new constraints. Modern robot imitation learning, in contrast, typically needs hundreds to thousands of demonstrations and still degrades under modest shifts in layout, geometry, object set or task constraints. We argue this gap is not just about data, but also about the level of abstraction at which learning occurs; generalization requires inferring the latent intent underlying why a demonstrator behaved in a certain way, rather than reproducing how they moved. We present Rational Inverse Reasoning (RIR), which casts few-shot imitation as inference over latent explanation programs: compact, executable descriptions of intent that map an object-centric scene to a structured task-and-motion-planning (TAMP) specification of goals, subgoals and constraints. A vision-language model proposes candidate programs, and a hierarchical planner supplies a bounded-rational likelihood. By combining VLM program proposals, and planner-grounded feedback, RIR iteratively refines the candidate set to approximate a posterior over concise, executable programs. On a 2D reasoning benchmark and a real Franka FR3, RIR recovers transferable task structure from as little as one demonstration. Generalizing to substantially new layouts and object sets, RIR outperforms VLM-planning baselines that lack explicit rationality and planning-grounded inference, increasing downstream success rate by

34

and

28

percentage points in the one- and three-shot settings.

Ben Zandonati, Tomás Lozano-Pérez, Leslie Pack Kaelbling

MIT CSAIL

Robot Skill Learning Generalization in Robotic Manipulation Program Synthesis Few-Shot Learning Robot Imitation Learning Robot Policy Generalization

Jul 15, 2025cs.RO

Robot Drummer: Learning Rhythmic Skills for Humanoid Drumming

Humanoid robots have seen remarkable advances in dexterity, balance, and locomotion, yet their role in expressive domains such as music performance remains largely unexplored. Musical tasks, like drumming, present unique challenges such as split-second timing, rapid contacts, and multi-limb coordination over performances lasting minutes. In this paper, we introduce Robot Drummer, a simulation framework for humanoid drumming across a diverse repertoire of songs. We formulate humanoid drumming as the realization of timed contact events encoded as a Rhythmic Contact Chain. To handle the long-horizon nature of musical performance, we decompose each track into fixed-length segments and train a single policy across all segments in parallel using reinforcement learning. Through extensive experiments on over thirty popular tracks, our results demonstrate that Robot Drummer consistently achieves high F1 scores and enables efficient learning of long-horizon musical performances. The learned behaviors exhibit emergent human-like drumming strategies, such as cross-arm strikes, and adaptive stick assignments, demonstrating the potential of reinforcement learning to bring humanoid robots into the domain of creative musical performance. Project page: robotdrummer.github.io

Asad Ali Shahid, Francesco Braghin, Loris Roveda

IDSIA, SUPSI, Lugano, Switzerland · Department of Mechanical Engineering, Politecnico di Milano, Italy

Long-Horizon Robotic Manipulation Robot Skill Learning Robotics Simulation Humanoid Robot Control Robotic RL

May 28, 2025cs.RO

Prior Reinforce: Goal-Conditioned Dynamic Manipulation with Limited Trials

Embodied robots have achieved strong performance in many real-world manipulation tasks, yet agile dynamic manipulation remains challenging due to high sensitivity to motion parameters and sparse outcome-level feedback. Tasks such as shooting a basketball into a hoop require precise control of fast open-loop motions, where small trajectory variations can lead to large outcome deviations, making data-efficient adaptation difficult for existing methods that rely on large-scale interaction, reward engineering, or accurate dynamic modeling. We propose Prior Reinforce (P.R.), a simple and practical framework for goal-conditioned dynamic manipulation. The method first learns a structured motion manifold from a small set of demonstrations using a conditional diffusion model, and then adapts motions toward new goals through feedback-driven optimization in a low-dimensional condition space. By separating motion generation from outcome-driven adaptation, the framework enables efficient refinement using only a small number of real-world trials under noisy perception. Experiments on multiple real-world dynamic manipulation tasks demonstrate that P.R. reliably achieves new goals within as few as ten total trials while remaining robust to perception noise and hardware uncertainty, suggesting a practical approach for low-trial real-world robot adaptation. Project website: https://adap-robotics.github.io/.

Yihang Hu, Pingyue Sheng, Yuyang Liu +2

IIIS, Tsinghua University. · Shanghai Qi Zhi Institute. · Spirit AI.

Robot Policy Adaptation Robot Skill Learning Robot Motion Generation Robotic Manipulation Dynamic Robotic Manipulation Robot Learning

Date pendingcs.RO

Dual-Process Atomic Skill Learning: Decoupling Semantic Reasoning and Real-Time Control

Language-conditioned Imitation Learning (IL) is essential for enabling robots to perform complex tasks following natural language instructions. However, generalizing to multi-step compositional tasks remains a significant challenge. While hierarchical approaches attempt to address this by decomposing tasks into atomic skills, existing methods often suffer from training instability and codebook collapse due to the tight coupling between high-level skill reasoning and low-level action generation in joint training paradigms. Inspired by the Dual-Process Theory of cognition, we propose Dual-Process Atomic Skill Learning (DASL), a novel asynchronous hierarchical imitation learning framework that decouples slow semantic reasoning from fast, real-time motion control. DASL comprises a Slow-Frequency Policy that predicts interpretable, discrete skills via Vector Quantization, and a High-Frequency Policy that leverages a latent diffusion model and a Decision Transformer to generate precise actions conditioned on these latent skills. By asynchronously coordinating these modules and utilizing diffusion to structure the latent space, our framework mitigates the skill codebook interference problem common in joint training paradigms. Evaluations across simulation benchmarks and experiment demonstrate that DASL significantly outperforms state-of-the-art baselines, excelling in skill acquisition and compositional generalization to unseen instructions. GitHub page: https://github.com/Hatakekaka/DASL

Jun Chen, Erdemt Bao, Wenlong Dong +7

University of Electronic Science and Technology of China · Huazhong University of Science and Technology · Southern University of Science and Technology +2

Robot Skill Learning Imitation Learning Robot Imitation Learning Language-Conditioned Robot Control

Date pendingcs.RO

tinyDSM: A Framework for Skill Modeling and Development for Resource-Constrained Millirobots

In this study, we investigate developmental mechanisms that enable small, resource-constrained systems such as cm-sized millirobots to autonomously explore, learn, and adapt their capabilities throughout their lifespan. Reinforcement learning algorithms guide the agent's skill acquisition and adaptation through the interplay of our proposed tiny Developmental Skill Method (tinyDSM), which integrates intrinsic motivation and fitness-based assessment. We strive for minimal hard-wired skills while encouraging the open-ended development of new skills. A key emphasis in our approach is to encode minimal a-priori general knowledge, which serves as a foundational starting point for the system as it further learns system-specific dependencies from the initial knowledge provided. Thus, by design, our approach aims to cover generic application domains. The methodology is based on (a) developmental mechanism with intrinsic motivation, and (b) a cognitive architecture (knowledge, reasoning, learning), while (c) utilizing minimal resources. It uses a hierarchical knowledge graph and kinematic reasoners to model and evaluate simple and advanced motion related skills. In our experiments, we use a resource-constrained millirobot with a volume of 36 cm^3 with a Raspberry Pi Pico 32-bit microcontroller that integrates all described features and capabilities except the camera system in 9 kB. Starting with learning the most elementary motor skills the millirobot autonomously progresses from simple linear and angular movements to complex geometric patterns within 15 minutes. To complement the physical experiments, we perform a simulation-based analysis that enables systematic comparisons across learning algorithms and intrinsic motivation parameters.

Markus Kobelrausch, Michael Miedler, Axel Jantsch

Institute of Computer Technology, TU Wien, 1040 Vienna, Austria

Robot Skill Learning Intrinsic Motivation Robotic RL Robotics Agent Skill Learning

Date pendingcs.RO

SkillX: Unified Multi-Skill Policy Learning for Humanoid Soccer

Humanoid soccer is a challenging testbed for dynamic whole-body control, requiring robots to coordinate balance, locomotion, object interaction, and skill switching over long horizons. Existing humanoid sports methods often rely on task-specific multi-stage pipelines, making it difficult to jointly learn and compose multiple object-interactive skills within a single deployable policy. To address this, we present SkillX, a unified reinforcement learning framework that learns and composes multiple atomic soccer skills through a single command-conditioned policy. SkillX integrates three core designs: skill-specific adversarial motion priors, skill-specific critics, and an object-aware temporal encoder, enabling the robot to execute atomic skills and transition among them such as dribbling, trapping, and shooting. Experiments in simulation and on a real Noetix E1 humanoid demonstrate robust multi-skill execution, long-horizon skill composition, and successful sim-to-real deployment.

Zhangchen Ye, Enxuan Ruan, Yifei Bao +10

Noetix Robotics · Tsinghua University

Robot Skill Learning Humanoid Robot Control Robotics Multi-Task RL Agent Skill Learning

Date pendingcs.RO

Compact Visuotactile World Models for Lifting: Prediction, Reward Alignment, and Force Constraints

Accurate tactile forecasts need not improve force-constrained control. We study a 652,157-parameter action-conditioned visuotactile world model with matched behavior cloning, policy learning in imagination, independent reactive implicit Q-learning, and model-assisted force feedback. A fixed protocol executes 34 policies on 120 fresh MuJoCo environments spanning geometry and physical-parameter shifts, plus 324 independently replayed action branches on 12 additional ID environments. Visuotactile dynamics reduce force action-effect MAE from 0.413 N for persistence to 0.338 N. Model-assisted feedback raises ID force-budgeted success from 73.3% to 93.3%, with paired difference +20.0 [+6.7,+33.4] percentage points (95% CI), with the difference occurring during scripted lowering. Its pooled difference is +3.9 [-4.5,+11.7] points. Imagined RL achieves 11.9% pooled joint success versus 25.0% for reactive IQL. An empirical tactile-residual stress test adds 330 executions. The evidence concerns rigid-box lifting after a common approach, without physical-robot transfer or a closed-loop safety guarantee.

Qinzhen Ma (Rice University)

Rice University, USA

Robot Skill Learning World Models for Robotics Robotic Manipulation Force-Controlled Robotic Manipulation Tactile Sensing for Robotic Manipulation Model-Based RL+1