Vision-language-action (VLA) models are a promising approach to general purpose robot manipulation. While the internal computation of vision-language models, which compose the backbone of VLAs, has been studied extensively, less is known about how their perceptual and semantic representations are transformed into actio...
Aiden Swann, Mathilde Papillon, Mozes Jacobs et al.· 0 citations
State-of-the-art visuomotor policies are increasingly capable of executing complex tasks, yet can fail during deployment in ways they cannot predict or recover from on their own. Improving their performance and robustness typically requires collecting more data, retraining, or modifying the policy architecture, all of...
Colton Crosby, Hao Wang, Mac Schwager et al.· 0 citations
This study investigates Physical Recursive Self-Improvement (Physical RSI), in which physical experience guides lasting updates to both a robot's capabilities and its improvement procedure, making subsequent improvement more effective across tasks.
Hai Zhou, Fan Yang, Ze-Yi Chen et al.· 0 citations
Results show that explicit concepts provide a reusable and extensible memory interface for robot control and motivate ECoMEM, which represents task-relevant history with a reusable library of grounded concepts.
Yi-Ze Liu, Ke Wang, Mac Schwager et al.· 0 citations
Autonomously constructing physically realizable 3D structures remains a significant challenge due to combinatorial action spaces, interchangeable components, equifinal assembly sequences, and strict stability requirements during construction. State-of-the-art methods fine-tune large language models for text-based gener...
Gadiel Sznaier Camps, Cheng-Yang He, G. Sartoretti et al.· 0 citations
We present PRIMAL3, an ultra-large-scale learning-based framework for multi-agent pathfinding (MAPF) that integrates reinforcement learning, topology-aware communication, LaCAM3-guided training, and PIBT-based action refinement. PRIMAL3 targets failures at topologically critical states, where agents must coordinate dec...
Cheng-Yang He, T. Duhan, Gadiel Sznaier Camps et al.· 1 citation
UniMem is presented, a framework that unifies high-level, multimodal memory and low-level control under one backbone that outperforms fixed-interval image sampling baselines in simulation and hierarchical baselines in hardware, while offering faster inference and a simple training pipeline for easy adoption.
Lars W. Osterberg, M. Wang, Mac Schwager· 1 citation
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