collaborators

7 papers

cs.RO2026

Freeform Preference Learning for Robotic Manipulation

Marcel Torne, Anubha Mahajan, Abhijnya Bhat +1

Reward design remains a central bottleneck for autonomous robot policy improvement, especially in long-horizon manipulation tasks where sparse success labels provide too little sig…

cs.LG2026

: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities

Physical Intelligence, Bo Ai, Ali Amin +85

We present a new robotic foundation model, called , that can enable strong out-of-the-box performance in a wide range of scenarios. can follow diverse language i…

cs.RO2026

MEM: Multi-Scale Embodied Memory for Vision Language Action Models

Marcel Torne, Karl Pertsch, Homer Walke +14

Conventionally, memory in end-to-end robotic learning involves inputting a sequence of past observations into the learned policy. However, in complex multi-stage real-world tasks,…

cs.RO2025

PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies

Arhan Jain, Mingtong Zhang, Kanav Arora +11

A significant challenge for robot learning research is our ability to accurately measure and compare the performance of robot policies. Benchmarking in robotics is historically cha…

cs.LG2025

DISCO: A Browser-Based Privacy-Preserving Framework for Distributed Collaborative Learning

Julien T. T. Vignoud, Valérian Rousset, Hugo El Guedj +28

Data is often impractical to share for a range of well considered reasons, such as concerns over privacy, intellectual property, and legal constraints. This not only fragments the…

cs.LG2025

: a VLA That Learns From Experience

Physical Intelligence, Ali Amin, Raichelle Aniceto +53

We study how vision-language-action (VLA) models can improve through real-world deployments via reinforcement learning (RL). We present a general-purpose method, RL with Experience…

cs.RO2025

Learning Long-Context Diffusion Policies via Past-Token Prediction

Marcel Torne, Andy Tang, Yuejiang Liu +1

Reasoning over long sequences of observations and actions is essential for many robotic tasks. Yet, learning effective long-context policies from demonstrations remains challenging…