arXiv:2511. 20532v3 Announce Type: replace-cross Abstract: The primary output of the nervous system is movement and behavior.
By Charles Y. Zhang (Harvard University), Yuanjia Yang (Salk Institute for Biological Studies), Aidan Sirbu (Mila), Elliott T. T. Abe (University of Washington), Emil W\"arnberg (Harvard University), Eric J. Leonardis (Salk Institute for Biological Studies), Diego E. Aldarondo (Harvard University), Adam Lee (Harvard University), Aaditya Prasad (Massachusetts Institute of Technology), Jason Foat (Salk Institute for Biological Studies), Kaiwen Bian (Salk Institute for Biological Studies), Joshua Park (Salk Institute for Biological Studies), Rusham Bhatt (Salk Institute for Biological Studies), Vyom N. Patel (Neuromatch), Hutton Saunders (Salk Institute for Biological Studies), Austin O. Barbano (Salk Institute for Biological Studies), Akira Nagamori (Salk Institute for Biological Studies), Ayesha R. Thanawalla (Salk Institute for Biological Studies), Kee Wui Huang (Salk Institute for Biological Studies), Fabian Plum (Imperial College London), Hendrik K. Beck (Imperial College London), Steven W. Flavell (Massachusetts Institute of Technology), David Labonte (Imperial College London), Blake A. Richards (Mila), Bingni W. Brunton (University of Washington), Eiman Azim (Salk Institute for Biological Studies), Bence P. \"Olveczky (Harvard University), Talmo D. Pereira (Salk Institute for Biological Studies)
A central capability of embodied agents is to accomplish complex objectives through sequences of interdependent tasks. Yet existing visual goal-conditioned policies underlying these agents are typical...
The paper introduces TacEx, a tactile‑curiosity framework that guides reinforcement learning agents to explore contact dynamics by focusing epistemic uncertainty on the tactile channel. By anchoring curiosity to touch, robots learn to manipulate and grasp objects without task rewards or demonstrations, generating an interaction‑dense dataset that supports offline pick‑and‑place policy learning. TacEx also enhances vision‑language‑action models through post‑training, improving downstream performance while remaining sample‑efficient.
By Klemens Iten, Alexander Proshkin, Bhavya Sukhija, Stelian Coros, Andreas Krause, Pieter Abbeel, Carmelo Sferrazza
Attacca is a new approach for training visual goal‑conditioned policies that can handle continuous long‑horizon task execution in embodied agents. It decouples goal images from the execution environment by sampling class‑compatible masked goals from other worlds, enabling the policy to ground targets in the current view via a target‑mask prediction head. The method also introduces behavioral‑phase conditioning to distinguish Search, Approach, and Interact stages, and achieves significant performance gains on Minecraft tasks, outperforming baselines by up to 7× on long‑horizon scenarios.
By Gyusik Seo, Jaehong Yoon
arXiv:2410.03246v3 Announce Type: replace-cross
Abstract: Deep Reinforcement Learning (DRL) enables robots to learn complex behaviors through interaction with the environment. However, due to the unr...
By Oliver Hausd\"orfer, Alexander von Rohr, \'Eric Lefort, Angela Schoellig
The paper introduces Adaptive Agents via Latent Topologies (AALT), a method for active imitation learning that selects demonstrations based on their expected impact on start‑to‑goal connectivity rather than generic information gain. AALT builds a latent topology of hub states and learned behaviors, identifies high‑value bridge demonstrations that can solve many tasks simultaneously, and uses these to condition a diffusion policy for planning. In a simulated UR5e robot retrieval task with 72 start‑goal pairs, AALT achieved 100% success after only three demonstrations, outperforming baselines that required many more queries.
By Maxwell J. Jacobson, Ahmed H Qureshi, Yexiang Xue