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
arXiv:2610.20584v1 Announce Type: new
Abstract: Computer-use agents have demonstrated strong capabilities in interpreting screens and executing keyboard and mouse actions. However, games, with their...
By Mingyu Ouyang, Yiran Wang, Siyuan Hu, Xingyilang Yin, Huiqiang Sun, Guangzhi Wang, Junfu Pu, Cheng Cheng, Honghao Chen, Ying Shan, Mike Zheng Shou, Hwee Tou Ng
arXiv:2607. 00642v1 Announce Type: new Abstract: Reinforcement learning has proven to be a valuable tool in the creation of advanced AI and robotic systems, contributing to everything from game playing to robotics to foundation models.
By Roberto Capobianco (Sony AI, Zurich, Switzerland), Harm van Seijen (Sony AI, North America, various locations), Nolan D. Bard (Sony AI, North America, various locations), Neil Burch (Sony AI, North America, various locations), Fatima Davelouis (Sony AI, North America, various locations), Josh Davidson (Sony AI, North America, various locations), Alisa Devlic (Sony AI, Zurich, Switzerland), Yunshu Du (Sony AI, North America, various locations), Ishan Durugkar (Sony AI, North America, various locations), Siddhant Gangapurwala (Sony AI, North America, various locations), Daniel Hernandez (Sony AI, North America, various locations), G. Zacharias Holland (Sony AI, North America, various locations), Sahil Jain (Sony AI, North America, various locations), Kenta Kawamoto (Sony AI, Tokyo, Japan), Raksha Kumaraswamy (Sony AI, North America, various locations), Patrick MacAlpine (Sony AI, North America, various locations), Dustin R. Morrill (Sony AI, North America, various locations), Declan Oller (Sony AI, North America, various locations), Francesco Riccio (Sony AI, Zurich, Switzerland), Akanksha Saran (Sony AI, North America, various locations), Craig Sherstan (Sony AI, Tokyo, Japan), Kaushik Subramanian (Sony AI, Zurich, Switzerland), Thomas J. Walsh (Sony AI, North America, various locations), Samuel Barrett (Sony AI, North America, various locations), Kizza N. Frisbee (Sony AI, North America, various locations), Mady Govil (Sony AI, North America, various locations), Johannes G\"unther (Sony AI, North America, various locations), Varun R. Kompella (Sony AI, North America, various locations), James A. MacGlashan (Sony AI, North America, various locations), Maxwell Svetlik (Sony AI, North America, various locations), Michael D. Thomure (Sony AI, North America, various locations), Jaden B. Travnik (Sony AI, North America, various locations), Kevin Waugh (Sony AI, North America, various locations), Elahe Aghapour (Sony AI, North America, various locations), Florian Fuchs (Sony AI, Zurich, Switzerland), Andreanne Lemay (Sony AI, North America, various locations), Shruti Mishra (Sony AI, Zurich, Switzerland), Takuma Seno (Sony AI, Tokyo, Japan), Peter Stone (Sony AI, North America, various locations), Michael Spranger (Sony AI, Tokyo, Japan), Peter R. Wurman (Sony AI, North America, various locations)
arXiv:2510. 17059v2 Announce Type: replace Abstract: Zero-shot imitation learning requires an agent to reproduce expert behavior from a single demonstration without additional environment interaction or gradient updates at test time.
By Kathryn Wantlin, Chongyi Zheng, Benjamin Eysenbach
arXiv:2505. 04999v2 Announce Type: replace-cross Abstract: Learning robot control policies from demonstrations typically requires action-labeled expert data, which is expensive to collect through teleoperation.
By Anthony Liang, Pavel Czempin, Matthew M. Hong, Yutai Zhou, Jingzhen Wang, Erdem Biyik, Stephen Tu
arXiv:2606. 02194v1 Announce Type: new Abstract: Distilling expert demonstration data into large generative models using behavioral cloning is a scalable approach to learning capable policies for robotic control, particularly for dexterous manipulation.
By Christian Scherer, Joe Watson, Theo Gruner, Daniel Palenicek, Ingmar Posner, Jan Peters
arXiv:2605. 14211v3 Announce Type: replace Abstract: Long-horizon embodied tasks remain a fundamental challenge in AI, as current methods rely on hand-engineered rewards or action-labeled demonstrations, neither of which scales.
By Benjamin Schneider, Xavier Schneider, Victor Zhong, Sun Sun