Goal-Conditioned Robotic Manipulation

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17 papers2 papers in the last four weeks, down 50% on the four weeks before. 0.0% of all new papers.

Oct 7, 2026

Predicted Futures Are Not Enough: Learning Executable Goals for Robot Manipulation

Generative world models provide rich predictions of how manipulation scenes may evolve toward task objectives, yet those futures do not directly expose the compact task variables required by control. When training supervises future prediction alone, terminal goal accuracy is not an explicit learning objective, even when geometric recovery is available. We present Entity-Level Goal Readout, a learned prediction-to-execution interface that makes the executable terminal goal an explicit output of a 3D trace world model. It combines object-centric pose prediction with translation grounded in observed depth to produce a compact goal in SE(3). A shared Pose-Native Executor consumes this fixed goal with online object-pose feedback for closed-loop control without rerunning the world model. Across five manipulation tasks, the pipeline achieves a mean success rate of 79.69%. Goal diagnostics directly measure terminal goal accuracy, while controlled translation perturbations characterize how execution degrades under goal error. Zero-shot deployment on a Franka arm achieves 73.33% success on nominal StackCube, 66.67% with distractors, and 75.00% on PickPlate with a target unseen during policy training. These results support treating the prediction-to-execution interface as an explicit learned component of world-model planning rather than incidental post-processing in the control pipeline itself. Project page: https://claire0730.github.io/executable-goals/

Tzu-Yu Chuang, Ching-Hsiang Chang, Yi-Hsiu Lee +3

National Yang Ming Chiao Tung University · National Tsing Hua University

Robotic Manipulation Robot Task Planning Goal-Conditioned Robotic Manipulation World Models for Robotics Model-Based Planning

Sep 22, 2026

VisForce: Visual Grounding of Current and Desired Forces for Goal-Conditioned Dexterous Manipulation

Vision-Language-Action (VLA) models have emerged as general-purpose robotic manipulation policies. However, in dexterous hand manipulation, contact forces are typically provided as separate states or force-specific representations, making it difficult to explicitly represent the spatial correspondence between force and their corresponding visual locations. In this work, we propose VisForce, which visually grounds the current and desired forces at their corresponding fingertip locations. VisForce renders current and desired visual force cues on the current wrist image and a task-specific goal image, and combines the two representations through goal-conditioned cross-attention to generate force-aware actions. We evaluate VisForce using a real UR10 robot equipped with an RH56F1 dexterous hand through force-conditioned grasping and three multi-stage manipulation tasks. In force-conditioned grasping experiments, VisForce exhibited a consistent grip-force response as the desired force increased, and achieved grasp-and-lift success rates of 70% and 80% for an egg and a toothpaste tube, respectively. It further achieved final success rates of 70%, 55%, and 40% on cup insertion/bottle pouring, tong-assisted bread transfer, and slip-modulated peg-in-hole, respectively. These results show that fingertip-aligned visual force representations can be effectively used for force-aware conditioning in VLA-based dexterous hand manipulation.

Jung-Woo Lee, Soo-Chul Lim

Contact-Rich Robotic Manipulation Goal-Conditioned Robotic Manipulation Robotic Manipulation Force-Controlled Robotic Manipulation

Sep 9, 2026

Grounding Generated Video Plans in Simulation Towards Versatile Dexterous Controllers

Generated hand-object interaction (HOI) videos provide a controllable way to propose manipulation motions. Simulation-based HOI tracking can translate such kinematic references into feasible low-level control, but its scalability is limited by the lack of reliable reference motions. We therefore combine generated videos with simulation-based HOI grounding: during training, generated videos provide diverse motion references for learning a multi-object, multi-trajectory HOI tracker, and at deployment, the video model produces motion plans that are executed by the learned tracker. In particular, we propose a method that enables scalable reference generation by HOI reconstruction with minimal manual intervention and successfully grounds more than 1,500 generated videos in simulation, achieving success rates over 25 percentage points higher than those of baselines during simulation-based training. In real-world closed-loop experiments, it achieves diverse grasps, including functional grasps, non-prehensile manipulation, and post-grasp object-pose tracking. Videos and code are available at https://boyuan-an.github.io/GALATEA/.

Tianyue Wu, Boyuan An, Shuqi Zhao +6

Sharpa Robotics · The University of Hong Kong · University of California, Berkeley

Goal-Conditioned Robotic Manipulation Hand-Object Interaction Detection Robotic Manipulation Human-Object Interaction

Sep 7, 2026

GIFT: Goal-Injected Fine-Tuning for Efficient Manipulation Policy Adaptation

Compared with relying solely on initial observations and language instructions, predicting goal images with generative models as high-level visual guidance can significantly enhance the robustness of Vision-Language-Action (VLA) models. However, most existing foundation models have not systematically incorporated goal image conditioning due to the high computational training cost. To this end, we propose Goal-Injected Fine-Tuning (GIFT), a lightweight and efficient fine-tuning framework that seamlessly integrates generated goal images into multiple representative pretrained VLA models. Our approach introduces goal image features into observations via a zero-initialized convolution which progressively grows parameters from zero and prevents harmful noise from disrupting the pretrained policy during fine-tuning. As training proceeds, goal information is gradually incorporated, enabling efficient goal understanding without disrupting model stability. We further introduce a refined image editing method to generate semantically and visually consistent goal images from initial observations and task instructions. Experiments show that goal-aware VLA models achieve substantial performance gains across tasks: with only a single epoch of fine-tuning, GIFT outperforms the base model by 6.0% and 13.4% on two SIMPLER settings, and by 4.7% on LIBERO, demonstrating both efficiency and effectiveness.

Xiaoyuan Fang, Shuo Feng, Yuxuan Wang +3

College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, 211106, China · The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, 211106, China.

Goal-Conditioned Robotic Manipulation Vision-Language-Action Models Efficient VLA Models Robotic Manipulation

Sep 6, 2026

MemCorr-DP: Counterfactual Correspondence Conditioning for a Diffusion Policy Guided by a Reference

Behavior-cloned visuomotor policies can remain accurate near their training distribution yet fail when object position and camera viewpoint change together. A successful reference trajectory contains the geometry needed to transfer the same interaction, but the policy must align that geometry with the current scene and remain sensitive to it during denoising. To address these challenges, we present MemCorr-DP, a diffusion policy that lifts frozen RoMa v2 matches into explicit 3D relations between the current scene and the reference trajectory. A counterfactual paired objective assigns opposite behaviors the same physical state and noisy action while retaining reference-specific denoising targets. Mixed-condition fine-tuning then adapts the policy from ground-truth geometry to measured correspondence errors. Our strongest evaluation places the Door in the outermost position bands beyond the training support and changes the query camera by

\pm15^\circ

. Under this combined shift, MemCorr-DP achieves 96.67% closed-loop success, compared with 88.00% for a visual Transformer with the same action architecture. Objective ablations and reference interventions show that behavior responds to the selected reference, while matched controls favor the complete relation set over future motion or centroid geometry alone. These results support explicit 3D reference relations as a robust conditioning interface when spatial and viewpoint changes are compounded in the evaluated task.

Tan Su, Haoxiang Yang, Ruxin Wang +1

Southern University of Science and Technology · Sun Yat-sen University · Knowin

Goal-Conditioned Robotic Manipulation Diffusion Policy Robot Policy Generalization Robotic Manipulation

Sep 2, 2026

HINT: Human-Intent Inception for Long-Horizon Robot Manipulation

Humans can perform complex manipulations given a simple intent through an overall instruction, while continuously adapting to evolving visual observations. However, current vision-language action (VLA) models and other action policies struggle to realize this high-level intelligent behavior under dense, evolving visual inputs and sparse language guidance. Visual correlations can then dominate semantic intent, leading actions to follow visual shortcuts rather than human goals. We present HINT (Human-INTent INcepTion), an agentic framework inspired by the human manipulation principles: semantic intent changes sparsely at manipulation-pattern transitions, whereas continuous control primarily depends on the evolving object-hand relationship. HINT invokes semantic reasoning only at pattern transitions to resolve the current subtask and target, then maintains this commitment through multi-view grounding and visual tracking. We explore two visual interfaces-image-space semantic highlighting and attention-prior injection-to communicate the tracked intent to the action policy without introducing additional trainable parameters into the foundation action model. Experiments across three long-horizon tasks and out-of-distribution variants show that HINT substantially improves intent understanding, task progress, and end-to-end success across two foundation policies while preserving low-latency control. Project page: https://robot-hint.github.io/

Mingyu Mei, Haojie Xu, Shihao Jin +9

Zhejiang University · Shanghai Jiao Tong University · Noematrix +1

Goal-Conditioned Robotic Manipulation Long-Horizon Robotic Manipulation Robotic Manipulation Vision-Language-Action Models

Aug 22, 2026

Vision Guided Target Conditioned Control for Autonomous Excavation

Autonomous excavation requires an intelligent control system that can convert spatial work intent into coordinated bucket motion under contact-rich soil interaction. This paper presents a target-conditioned intelligent control framework for autonomous excavation in a physics-based deformable-soil simulation workflow. An image-aligned target mask serves as a visual spatial command for the desired digging region, while a mask-conditioned Action Chunking Transformer maps multi-view RGB observations, proprioception, and the target mask to temporally extended joystick commands. To reduce target-ignoring behavior, demonstrations are organized with paired-condition supervision, where the same or closely matched scene is demonstrated with different target masks and corresponding action chunks. The framework is evaluated through both a diagnostic manipulation task and an excavation simulation benchmark with single-scoop and sequential pile-clearing protocols. In manipulation, target success is 4% for no-condition ACT, 63% for non-paired mask-conditioned ACT, and 96% for paired-condition mask-conditioned ACT. In sequential pile clearing, paired-condition mask-conditioned ACT removes 76.8% of the pile versus 27.4% and 15.7% for the two baselines, with 91.0% human-normalized efficiency. The results show that visual target conditioning, paired demonstration structure, and action-chunk control form a practical cyber-physical simulation pipeline for excavator automation.

Shuai Zhao, Ji-An Pan, Junwei Li +5

Liaoning University, Shenyang, China · Northeastern University, Shenyang, China · Tsinghua University, Beijing, China

Robotic Excavation Goal-Conditioned Robotic Manipulation Vision-Based Robot Control Imitation Learning Robotic Manipulation

Aug 1, 2026

DynamicWAM: Dual-Path Motion Conditioning for World-Action Models in Dynamic Manipulation

Dynamic manipulation requires robots to infer target motion and respond promptly, yet existing World-Action Models (WAMs) typically condition only on the current frame and execute large backbones synchronously, limiting motion awareness and responsive control in dynamic scenes. We propose DynamicWAM, a compact WAM for dynamic object manipulation with dual-path motion conditioning. DynamicWAM introduces history-flow conditioning, encoding temporally aligned optical-flow frames alongside the current observation through a frozen pretrained video VAE to preserve spatial motion structure, while injecting kinematic descriptors of displacement, duration, velocity, and acceleration into the action expert to provide motion magnitude and timing. The two complementary paths are fused through joint world-action attention. A distilled compact backbone and real-time chunking (RTC)-based asynchronous execution further enable responsive control. On DOMINO, DynamicWAM achieves a 38.2% success rate and a 53.2 manipulation score, outperforming all evaluated baselines. Across 12 real-world tasks spanning linear, circular, and compound target motion, it achieves a 46.7% average success rate, exceeding the strongest baseline by 22.9 percentage points.

Yunfan Lou, Hewen Gao, Xiyu Zhu +6

PKU-PI Lab · National University of Singapore · ePyBot Intelligence +2

World Models for Robotics Goal-Conditioned Robotic Manipulation Robotic Manipulation Dynamic Robotic Manipulation

Jul 28, 2026

MoMo: Dial Motion Mode in Robot Manipulation with Spatiotemporal Action Tokenization

To operate effectively across diverse contexts, robots must not only perform manipulation tasks accurately but also adapt how their actions unfold to the task, object, and interaction setting. We ask whether this execution-level variation can be learned as a reusable behavioral factor shared across tasks. We present MoMo, a two-stage imitation-learning framework consisting of a spatiotemporal action tokenizer and a behavior-cloning transformer that takes task and a continuous motion-mode condition as inputs. Across six real-robot manipulation tasks, varying this condition produces steady, dynamic, and intermediate behaviors that human raters can distinguish and that differ in joint speed, acceleration, and end-effector approach pitch. On tasks demonstrated in only one mode, MoMo transfers the unseen requested mode while largely preserving task success. Together, these results provide evidence of compositional generalization to unseen task--mode combinations and show that motion mode can be reused across tasks to control how a manipulation skill is performed.

Yuhan Hu, Hugues Thomas, Peide Huang +3

Apple

Robot Skill Learning Generalization in Robotic Manipulation Goal-Conditioned Robotic Manipulation Robotic Manipulation Robot Imitation Learning

Jun 28, 2026

CORE: Common Outcome Regularities from Action-Free Visual Demonstrations for Robot Manipulation

Robot imitation learning often relies on costly robot demonstrations, while abundant action-free visual demonstrations, such as human videos, are difficult to use because they lack robot-executable actions and suffer from embodiment gaps. We propose CORE, a policy learning framework that extracts Common Outcome Regularities from visual demonstrations. Rather than transferring explicit actions across embodiments, CORE exploits a key observation: although successful trajectories for the same task can be diverse, their terminal states often share stable object configurations, spatial relations, and contact constraints. CORE first trains a terminal outcome encoder with contrastive and auxiliary temporal objectives, then aggregates successful terminal embeddings into visual goal prototypes, and finally injects these prototypes as global goal conditions into robot policies. Compared with language instructions, visual goal prototypes provide more concrete geometric and physical constraints for task completion. Across Meta-World, RoboTwin 2.0, and real-world manipulation, CORE improves the average success rate of the corresponding policy backbones by up to +3.9, +11.1, and +17.0 percentage points, respectively, and outperforms text-conditioned variants under the evaluated settings.

Juyi Sheng, Jincheng Li, Mingxin Tan +1

1Peking University, Shenzhen Graduate School, Shenzhen, China

Robotic Manipulation Goal-Conditioned RL Cross-Embodiment Robot Learning Robot Skill Learning Learning from Demonstration Robot Imitation Learning+1

Jun 11, 2026

GIVE: Grounding Human Gestures in Vision-Language-Action Models

Human communication is inherently multimodal, where language is often accompanied by non-verbal cues such as gestures to convey intentions. However, current Vision-Language-Action (VLA) models treat robotic manipulation as a pure text-driven task, overlooking the important role of gestures in Human-Robot Interaction (HRI). This often leads to inaccurate intent grounding and unreliable manipulation when language instructions are ambiguous or underspecified. To address this challenge, we propose GIVE (Gesture Intent via Visual-Semantic Enhancement), an effective approach that enhances pre-trained VLA models with human gesture understanding without architectural modifications. Specifically, GIVE incorporates gesture information through two complementary pathways: a visual pathway that overlays hand skeletons and fingertip rays onto robot observations for explicit object grounding, and a semantic pathway that generates high-level descriptions of human gestures and task instructions for robust intent grounding. By jointly leveraging visual and semantic guidance, GIVE enables VLA policies to better associate gestures with manipulation behaviors and adapt to dynamic interaction intents. In real-world HRI experiments, GIVE substantially outperforms the baseline, improving target object recognition accuracy by 40% and overall task success rate by 80%, while demonstrating strong robustness and generalization to unseen spatial layouts and diverse participants.

Pengfei Liu, Gen Li, Junqiao Fan +4

1MARS Lab, Nanyang Technological University

Language-Conditioned Robot Manipulation Goal-Conditioned Robotic Manipulation Vision-Language Grounding Robotic Manipulation Vision-Language-Action Models Human-Robot Interaction

Jun 8, 2026

GHOST: Hierarchical Sub-Goal Policies for Generalizing Robot Manipulation

We present GHOST, a framework for learning visuomotor manipulation policies that generalize beyond the training distribution. GHOST factorizes control into (i) a high-level policy that predicts the next sub-goal as a distribution over 3D end-effector poses from multi-view RGB-D observations, and (ii) a low-level goal-conditioned controller that executes embodiment-specific actions. To condition image-based policies on 3D goals, we introduce a simple spatial interface that projects predicted goals into the image plane and represents them as end-effector heatmaps. Across a suite of manipulation tasks, this hierarchical factorization consistently improves performance and robustness compared to a flat Diffusion Policy. Further, we show that this hierarchical interface also makes it easy to incorporate human demonstrations without relying on (noisy) action retargeting. As sub-goals are largely embodiment-agnostic, we train the high-level policy on human video to specify how learned skills should be applied and composed, while keeping the low-level policy trained purely on robot data. This hierarchy enables adaptation to novel objects and task variations using a small number of human demonstrations.

Sriram Krishna, Ben Eisner, Haotian Zhan +5

Robotics Institute, Carnegie Mellon University · UMass Amherst

Robot Policy Learning Robot Skill Learning Generalization in Robotic Manipulation Visuomotor Policy Learning Robot Imitation Learning Robot Policy Generalization+4

May 21, 2026

GesVLA: Gesture-Aware Vision-Language-Action Model Embedded Representations

Vision-Language-Action (VLA) models have shown strong potential for general-purpose robot manipulation by unifying perception and action. However, existing VLA systems primarily rely on textual instructions and struggle to resolve spatial ambiguity in complex scenes with multiple similar objects. To address this limitation, we introduce gesture as a parallel instruction modality and propose a Gesture-aware Vision-Language-Action model (GesVLA). Our approach encodes gesture features directly into the latent space, enabling them to participate in both high-level reasoning and low-level action generation, and adopts a dual-VLM architecture to achieve tight coupling between gesture representations and action policies. At the data level, we construct a scalable gesture data generation pipeline by rendering hand models onto real-world scene images. This reduces the sim-to-real visual gap while producing rich data with diverse motion patterns and corresponding pointing annotations. In addition, we employ a two-stage training strategy to equip the model with both gesture perception and action prediction capabilities. We evaluate our approach on multiple real-world robotic tasks, including a controlled block manipulation task for validation and more practical scenarios such as product and produce selection. Experimental results show that incorporating gesture consistently improves target grounding accuracy and human-robot interaction efficiency, especially in complex and cluttered environments. Project page: https://gwxuan.github.io/GesVLA/.

Wenxuan Guo, Ziyuan Li, Meng Zhang +7

1Tsinghua University · 2Dexmal

Multimodal Grounding Goal-Conditioned Robotic Manipulation Robotic Manipulation Vision-Language-Action Models Human-Robot Interaction

Apr 12, 2026

AnySlot: Goal-Conditioned Vision-Language-Action Policies for Zero-Shot Slot-Level Placement

Vision-Language-Action (VLA) policies have emerged as a versatile paradigm for generalist robotic manipulation. However, precise object placement under compositional language remains challenging for end-to-end VLA policies. Slot-level placement requires reliable slot grounding and centimeter-level geometric precision. To this end, we propose AnySlot, a framework that reduces compositional complexity by introducing an explicit spatial visual goal between language grounding and control. AnySlot converts language into a visual goal by rendering a spatial marker at the intended slot, then executes this goal with a goal-conditioned VLA policy. This hierarchical design decouples high-level slot selection from low-level execution, improving semantic accuracy and spatial robustness. Furthermore, recognizing the lack of benchmarks for such precision-demanding tasks, we introduce SlotBench, a structured simulation benchmark with nine task categories for evaluating spatial reasoning in slot-level placement. Extensive experiments show that AnySlot significantly outperforms flat VLA baselines and modular grounding methods in zero-shot slot-level placement.

Zhaofeng Hu, Sifan Zhou, Qinbo Zhang +4

Stony Brook University, Stony Brook, USA · Carnegie Mellon University, Pittsburgh, USA · MBZUAI, Abu Dhabi, UAE +1

Goal-Conditioned Robotic Manipulation Robotic Manipulation Language-Conditioned Robot Manipulation Zero-Shot Robotic Manipulation Vision-Language-Action Models Spatial Grounding for VLA Models+1

Mar 2, 2026

ACDC: Adaptive Curriculum Planning with Dynamic Contrastive Control for Goal-Conditioned Reinforcement Learning in Robotic Manipulation

Goal-conditioned reinforcement learning has shown considerable potential in robotic manipulation; however, existing approaches remain limited by their reliance on prioritizing collected experience, resulting in suboptimal performance across diverse tasks. Inspired by human learning behaviors, we propose a more comprehensive learning paradigm, ACDC, which integrates multidimensional Adaptive Curriculum (AC) Planning with Dynamic Contrastive (DC) Control to guide the agent along a well-designed learning trajectory. More specifically, at the planning level, the AC component schedules the learning curriculum by dynamically balancing diversity-driven exploration and quality-driven exploitation based on the agent's success rate and training progress. At the control level, the DC component implements the curriculum plan through norm-constrained contrastive learning, enabling magnitude-guided experience selection aligned with the current curriculum focus. Extensive experiments on challenging robotic manipulation tasks demonstrate that ACDC consistently outperforms the state-of-the-art baselines in both sample efficiency and final task success rate.

Xuerui Wang, Guangyu Ren, Tianhong Dai +4

Cornell University, Ithaca, NY 14853, USA · Xi’an Jiaotong-Liverpool University, Suzhou, China · Applied Computing, UK

Goal-Conditioned RL Goal-Conditioned Robotic Manipulation Curriculum RL Data-Efficient Robot Learning Curriculum Learning Robotic Manipulation+1

Sep 21, 2025

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 Goal-Conditioned Robotic Manipulation Robotic Manipulation Robot Imitation Learning

May 28, 2025

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 Goal-Conditioned Robotic Manipulation Robot Motion Generation Robotic Manipulation Dynamic Robotic Manipulation+1