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
A robot may lose sight of an object it must later retrieve, need to recall what a person demonstrated earlier, or track which steps of a task it has already completed. Current vision-language-action (VLA) policies often fail once the information needed for action disappears from the current observation, making memory c...
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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