Robot Policy Generalization

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D-CLING: Prior-Preserving Depth-Conditioned Fine-Tuning for Navigation Foundation Models
May 19, 2026Shintaro Nakaoka, Takayuki Kanai, Kazuhito TanakaVisual Navigation Fine-Tuning
The authors are with the Frontier Research Center, Toyota Motor Corporation, Toyota, Aichi, Japan.
Robo-Cortex: A Self-Evolving Embodied Agent via Dual-Grain Cognitive Memory and Autonomous Knowledge Induction
May 18, 2026Nga Teng Chan, Yi Zhang, Yechi Liu +9Embodied Navigation World Model-Based Planning
1The Hong Kong University of Science and Technology · 2X-Humanoid · Institute of Automation, CAS +1
From a Single Demonstration to a General Policy for Contact-Rich Manipulation
May 17, 2026Xing Li, Oliver BrockRobot Skill Learning Contact-Rich Robotic Manipulation
Robotics and Biology Laboratory, Technische Universität Berlin · Technische Universität Berlin · Science of Intelligence, Research Cluster of Excellence, Berlin +1
DyGRO-VLA: Cross-Task Scaling of Vision-Language-Action Models via Dynamic Grouped Residual Optimization
May 17, 2026Sixu Lin, Yunpeng Qing, Litao Liu +4Vision-Language-Action Models Multi-Task RL
School of Data Science, The Chinese University of Hong Kong (Shenzhen) · Jiangxing Intelligence Technology Inc. · Zhejiang University +3
Feedback World Model Enables Precise Guidance of Diffusion Policy
May 15, 2026Tuo An, Jindou Jia, Gen Li +8World Model Learning World Models for Robotics
1MARS Lab, Nanyang Technological University
SID: Sliding into Distribution for Robust Few-Demonstration Manipulation
May 13, 2026Yicheng Ma, Wei Yu, Zhian Su +2Generalization in Robotic Manipulation Visuomotor Policy Learning
1Grasp Lab, Zhejiang University · 2Torch Kernel Co., Ltd.
When Absolute State Fails: Evaluating Proprioceptive Encodings for Robust Manipulation
May 13, 2026Maxime Alvarez, Ryo Watanabe, Paul Crook +4Robot Policy Learning Generalization in Robotic Manipulation
TELEXISTENCE Inc, Foundation Model Division, Japan. · The University of Tokyo, Japan.
SEVO: Semantic-Enhanced Virtual Observation for Robust VLA Manipulation via Active Illumination and Data-Centric Collection
May 11, 2026Tianchonghui Fang, Yuan Zhuang, Fei MiaoRobotic Data Collection Robotic Manipulation
School of Computing, University of Connecticut, Storrs, CT 06268, USA
HarmoWAM: Harmonizing Generalizable and Precise Manipulation via Adaptive World Action Models
May 11, 2026Qiuxuan Feng, Jiale Yu, Jiaming Liu +8World Models for Robotics Action-Conditioned World Models
State Key Laboratory of Multimedia Information Processing, School of Computer Science, Peking University · The Chinese University of Hong Kong · Simplexity Robotics
Higher Resolution, Better Generalization: Unlocking Visual Scaling in Deep Reinforcement Learning
May 11, 2026Raphael Trumpp, Ömer Veysel Çağatan, Barış Akgün +1RL Benchmarks Robot Policy Generalization
TUM School of Engineering and Design · Technical University of Munich · KUIS AI Center +2
HeteroGenManip: Generalizable Manipulation For Heterogeneous Object Interactions
May 11, 2026Zhenhao Shen, Zeming Yang, Yue Chen +5Robotic Grasping Robot Foundation Models
1Peking University · 2Tianjin University
Beyond Isolation: A Unified Benchmark for General-Purpose Navigation
May 10, 2026Samson Sun, Tianyi Yang, Tengyue Wang +6Embodied Navigation Cross-Embodiment Robot Learning
1AutoLab, SAI, Shanghai Jiao Tong University · 2Research Lab, Anyverse Dynamics
Enhancing RL Generalizability in Robotics through SHAP Analysis of Algorithms and Hyperparameters
May 4, 2026Lingxiao Kong, Cong Yang, Oya Deniz Beyan +1RL for Robotics Reinforcement Learning
Fraunhofer Institute for Applied Information Technology FIT, Germany · Soochow University, China · University of Cologne, Germany +1
Beyond Specialization: Robust Reinforcement Learning Navigation via Procedural Map Generators
May 4, 2026Christian Jestel, Nicolas Bach, Marvin Wiedemann +2Robot Navigation Robot Policy Generalization
Fraunhofer Institute for Material Flow and Logistics IML, Dortmund, Germany · The Lamarr Institute for Machine Learning and Artificial Intelligence, Germany · Westphalian University of Applied Sciences, Gelsenkirchen, Germany
LLM-Foraging: Large Language Models for Decentralized Swarm Robot Foraging
May 2, 2026Peihan Li, Joanna Gutierrez, Fabian Hernandez +2Swarm Robotics Language Model-Based Control
Drexel University, Philadelphia, PA 19104, USA · The University of Texas Rio Grande Valley, Edinburg, Texas 78541, USA
Decompose and Recompose: Reasoning New Skills from Existing Abilities for Cross-Task Robotic Manipulation
May 2, 2026Xitie Zhang, Aming Wu, Yahong HanICL for Robotics Zero-Shot Robotic Manipulation
School of Artificial Intelligence, College of Intelligence and Computing, Tianjin University, China · School of Computer Science and Information Engineering, Hefei University of Technology, China
Learning While Deploying: Fleet-Scale Reinforcement Learning for Generalist Robot Policies
May 1, 2026Yi Wang, Xinchen Li, Pengwei Xie +13Robot Policy Learning Robotic Manipulation
Shanghai Innovation Institute. · AGIBOT Finch. · Columbia University.
ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control
Apr 30, 2026Yanghao Zhou, Jingyu Ma, Yibo Peng +3Humanoid Robot Control Language-Conditioned Robot Control
Beijing Academy of Artificial Intelligence (BAAI)
SpecRLBench: A Benchmark for Generalization in Specification-Guided Reinforcement Learning
Apr 27, 2026Zijian Guo, İlker Işık, H. M. Sabbir Ahmad +1RL Benchmarks Reinforcement Learning
Boston University
Learning from Demonstration with Failure Awareness for Safe Robot Navigation
Apr 25, 2026Xianghui Wang, Siwei Cheng, Shanze Wang +3Safe Robot Navigation Offline RL
Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong · College of Information Science and Technology, Eastern Institute of Technology, Ningbo, China · School of Computer Science and Technology, University of Science and Technology of China, Hefei, China +1
RecoverFormer: End-to-End Contact-Aware Recovery for Humanoid Robots
Apr 24, 2026Zihui LiuHumanoid Robot Locomotion Humanoid Robot Control
Stanford University
VistaBot: View-Robust Robot Manipulation via Spatiotemporal-Aware View Synthesis
Apr 23, 2026Songen Gu, Yuhang Zheng, Weize Li +6Generalization in Robotic Manipulation Novel View Synthesis
1Fudan University · 2TARS Robotics · 4NUS +1
From Noise to Intent: Anchoring Generative VLA Policies with Residual Bridges
Apr 23, 2026Yiming Zhong, Yaoyu He, Zemin Yang +5Diffusion Policy Robot Motion Generation
ShanghaiTech University, Shanghai, China · Morphic Robotics, Shenzhen, China · The Chinese University of Hong Kong, Hong Kong, China.
AdaTracker: Learning Adaptive In-Context Policy for Cross-Embodiment Active Visual Tracking
Apr 22, 2026Kui Wu, Hao Chen, Jinzhu Han +6Visual Object Tracking Cross-Embodiment Robot Learning
Beihang University, Beijing, China · City University of Macau, Macau, China · School of Artificial Intelligence, Beijing Normal University, Beijing, China +3
JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy
Apr 22, 2026Tianle Zhang, Zhihao Yuan, Dafeng Chi +59Robot Skill Learning Robot Foundation Models
Mask World Model: Predicting What Matters for Robust Robot Policy Learning
Apr 21, 2026Yunfan Lou, Xiaowei Chi, Xiaojie Zhang +9Robot Policy Learning Video Diffusion Models
National University of Singapore, Singapore · Beijing Academy of Artificial Intelligence, Beijing, China · The Hong Kong University of Science and Technology, Hong Kong, China +3
From Seeing to Simulating: Generative High-Fidelity Simulation with Digital Cousins for Generalizable Robot Learning and Evaluation
Apr 17, 2026Jasper Lu, Zhenhao Shen, Yuanfei Wang +8Robotics Simulation Synthetic Data Generation for Robotics
1Peking University · 2Lightwheel
Zero-Shot Scalable Resilience in UAV Swarms: A Decentralized Imitation Learning Framework with Physics-Informed Graph Interactions
Apr 17, 2026Huan Lin, Lianghui DingMulti-Robot Systems Swarm Robotics
Institute of Image Communication and Network Engineering, School of Integrated Circuits, Shanghai Jiao Tong University, Shanghai 200240, China
ShapeGen: Robotic Data Generation for Category-Level Manipulation
Apr 16, 2026Yirui Wang, Xiuwei Xu, Angyuan Ma +3Robotic Data Collection Generalization in Robotic Manipulation
Tsinghua University
$π_{0.7}$ : a Steerable Generalist Robotic Foundation Model with Emergent Capabilities
Apr 16, 2026Physical Intelligence, Bo Ai, Ali Amin +85Robot Policy Learning Robot Skill Learning

May 19, 2026, cs.ROJ/K move · Enter open · S save

D-CLING: Prior-Preserving Depth-Conditioned Fine-Tuning for Navigation Foundation Models

Shintaro Nakaoka, Takayuki Kanai, Kazuhito Tanaka

The authors are with the Frontier Research Center, Toyota Motor Corporation, Toyota, Aichi, Japan.

Visual Navigation Fine-Tuning Robot Navigation Robot Policy Generalization

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Navigation Foundation Models (NFMs) trained on large cross-embodied datasets have demonstrated powerful generalizability in various scenarios. Adopting in-domain fine-tuning for an NFM efficiently calibrates the visuomotor policy, promising further improvement even in a novel scenario. However, the fine-tuned models still suffer from poor obstacle avoidance or fail to properly reach the provided goals. Furthermore, model updates using a small subset of data typically erode the pre-trained prior, compromising the pre-training generalization. Consequently, fine-tuning deteriorates the capability of the model for robust and accurate navigation. In this work, we present a novel fine-tuning method that leverages large-scale pre-training while efficiently learning in novel setups, such as environments or camera configurations. In particular, inspired by ControlNet, we fine-tune an NFM by attaching a trainable copy of the pre-trained backbone using zero-initialized residual pathways, thereby learning geometric cues. This design enables the model to efficiently acquire in-domain geometry while preserving pre-trained knowledge across various behaviors. Despite its simplicity, our comprehensive evaluation of real-world navigation suggests that our proposal effectively enables robust long-horizon navigation with minimal collisions and human intervention. Additionally, our offline analysis shows that the proposed method maintains or further improves action prediction capabilities beyond the fine-tuned dataset, providing a key insight into continual learning for general navigation. The project page: https://toyotafrc.github.io/DCLING-Proj/

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