Topic
imitation learning
RoboSSM Introduces State-Space Models for Scalable In-Context Imitation Learning in Robotics
RoboSSM is a new method for in-context imitation learning (ICIL) that replaces Transformer-based architectures with state-space models (SSMs). The approach uses Longhorn, a state-of-the-art SSM, enabling linear-time inference and strong extrapolation to longer prompts. Experiments on the LIBERO benchmark show improved generalization to unseen and long-horizon tasks compared to Transformer-based ICIL methods.
Self-Play RL with 30 Minutes of Human Data Trains Coordinated Driving Policies
A new approach from researchers trains autonomous driving policies using self-play reinforcement learning regularized by only 30 minutes of human demonstrations. The method requires 2500x less human data than imitation learning and completes training in 15 hours on a single consumer-grade GPU. The resulting policies successfully coordinate with held-out human trajectories, avoiding the alien driving conventions common in pure self-play systems.
FlowMPC: New Framework Combines Flow Matching and World Models to Improve Robot Manipulation
Researchers introduce FlowMPC, a framework that pairs imitation-learned flow matching policies with a learned world model for test-time planning using MPPI. On ManiSkill manipulation tasks PickCube and PickSingleYCB, adding the world model improved performance over the flow matching policy alone, with clear gains in end-of-episode success.