Toyota Research Institute Robotics

Long-running real-world robotics program with a public publication archive and current manipulation work.

No.Paper · what it does and showsIndustry contribution

Dreamitate: Real-World Visuomotor Policy Learning via Video Generation2026 ↗

Fine-tunes a video diffusion model on human demonstrations (CoRL 2024), generates an imagined execution conditioned on images of a novel scene, and uses that video directly to control the robot, with common tools bridging the human-to-robot gap.

Showed internet-scale video generators can serve as policy engines, cutting the demonstrations needed to deploy a known task in a new environment.

A Careful Examination of Large Behavior Models for Multitask Dexterous Manipulation2025 ↗

Extends Diffusion Policy into multitask Large Behavior Models and evaluates them through blind, randomized simulation and real-world trials, finding that multitask pretraining improves success, robustness and data efficiency.

Replaced demo-video evidence with blinded randomized trials as the bar for trusting robot foundation models, giving buyers a defensible method for evaluating them.

Diffusion Policy: Visuomotor Policy Learning via Action Diffusion2023 ↗

Represents a visuomotor policy as a denoising diffusion process over action sequences conditioned on observations, handling multimodal demonstrations and high-frequency control.

Became a default imitation-learning algorithm in both commercial manipulation stacks and open-source robot-learning libraries.