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Tool as Embodiment for Recursive Manipulation
Yuki Noguchi, Tatsuya Matsushima, Yutaka Matsuo +1
Humans and many animals exhibit a robust capability to manipulate diverse objects, often directly with their bodies and sometimes indirectly with tools. Such flexibility is likely…
VaxNeRF: Revisiting the Classic for Voxel-Accelerated Neural Radiance Field
Naruya Kondo, Yuya Ikeda, Andrea Tagliasacchi +3
Neural Radiance Field (NeRF) is a popular method in data-driven 3D reconstruction. Given its simplicity and high quality rendering, many NeRF applications are being developed. Howe…
Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains
Xin Zhang, Shixiang Shane Gu, Yutaka Matsuo +1
Domain generalization (DG) is a difficult transfer learning problem aiming to learn a generalizable model for unseen domains. Recent foundation models (FMs) are robust to many dist…
Generalized Decision Transformer for Offline Hindsight Information Matching
Hiroki Furuta, Yutaka Matsuo, Shixiang Shane Gu
How to extract as much learning signal from each trajectory data has been a key problem in reinforcement learning (RL), where sample inefficiency has posed serious challenges for p…
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization
Shixiang Shane Gu, Manfred Diaz, Daniel C. Freeman +7
The goal of continuous control is to synthesize desired behaviors. In reinforcement learning (RL)-driven approaches, this is often accomplished through careful task reward engineer…
Variational Empowerment as Representation Learning for Goal-Based Reinforcement Learning
Jongwook Choi, Archit Sharma, Honglak Lee +2
Learning to reach goal states and learning diverse skills through mutual information (MI) maximization have been proposed as principled frameworks for self-supervised reinforcement…