Richard Li

I am a PhD researcher at MIT CSAIL, where I work on robotics and machine learning.

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My research interest lies in finding a scalable path to robotics foundation models. I believe this will require solving cross-embodiment learning—especially transferring knowledge from human videos —as well as developing reinforcement learning methods that scale to large, real-world robotics datasets.

Hand pose quality and embodiment-specific network specialization are key to enabling transfer from everyday videos to robots.

Forecasting bimanual 3D hand motion and articulation from a single image using a diffusion-based lifting and forecasting pipeline.

Predicting contact points with a CVAE and plane segmentation improves object generalization and handles multimodality.

Multi-object, long-horizon manipulation can be autonomously learned using a curriculum and graph neural network architecture.

Synthesizing Lyapunov controllers through contact with alternating optimization.

Vision-based fingertip pose estimation with internal camera and CNN pose estimator.