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CometVLA: Co-Training on an Embodied Data Pyramid towards Physical Understanding
Hanwen Wan, Dafeng Chi, Linbo Zhai +6
Vision-language-action (VLA) models remain brittle in manipulation tasks that require physical commonsense. Current physical VQA data is typically disembodied and misaligned with r…
WorldSimProbe: Diagnosing Simulator Faithfulness in Action-Conditioned World Models for Embodied Manipulation
Peterson Co, Sicheng Hu, Chunxuan Jiao +17
Action-conditioned world models (ACWMs) promise to provide embodied AI with scalable predictive simulators for planning, policy evaluation, and data generation. Realizing this prom…
PACE: Phase-Aware Chunk Execution for Robot Policies with Action Chunking
Junnan Nie, Jiayi Li, Jiachen Zhang +5
Recent vision-language-action and diffusion-based robot policies often use action chunking, where each policy query predicts a sequence of future actions and the robot executes an…
JoyAI-Sim: A Simulation-Enabled Interconversion Toolchain for the Embodied Data Pyramid
Peidong Liu, Yongce Liu, Songyan Guo +34
Generalist robot policies require trustworthy evaluation and robot-usable training data, but both are difficult to scale with physical robots alone. Real-robot trials and demonstra…
CO-RFT: Efficient Fine-Tuning of Vision-Language-Action Models through Chunked Offline Reinforcement Learning
Dongchi Huang, Zhirui Fang, Tianle Zhang +3
Vision-Language-Action (VLA) models demonstrate significant potential for developing generalized policies in real-world robotic control. This progress inspires researchers to explo…
Object-Focus Actor for Data-efficient Robot Generalization Dexterous Manipulation
Yihang Li, Tianle Zhang, Xuelong Wei +7
Robot manipulation learning from human demonstrations offers a rapid means to acquire skills but often lacks generalization across diverse scenes and object placements. This limita…
Dexterous Grasping with Real-World Robotic Reinforcement Learning
Dongchi Huang, Tianle Zhang, Yihang Li +5
Dexterous grasping in the real world presents a fundamental and significant challenge for robot learning. The ability to employ affordance-aware poses to grasp objects with diverse…
Empowering Embodied Manipulation: A Bimanual-Mobile Robot Manipulation Dataset for Household Tasks
Tianle Zhang, Dongjiang Li, Yihang Li +8
The advancements in embodied AI are increasingly enabling robots to tackle complex real-world tasks, such as household manipulation. However, the deployment of robots in these envi…