Richard Li

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

Projects

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.

	What Matters When Cotraining Robot Manipulation Policies on Everyday Human Videos? Richard Li, Aditya Prakash, Andrew Wen, Saurabh Gupta, Yilun Du, Pulkit Agrawal In submission project page Hand pose quality and embodiment-specific network specialization are key to enabling transfer from everyday videos to robots.
	Prompt-Driven Exploration for VLA Policies Sunshine Jiang, John Marangola, David Zhang, Raghuram Kowdeed, Ruiyang Luo, Nitish Dashora, Richard Li, Pulkit Agrawal, Zhang-Wei Hong ICRA 2026 Workshop on VLA Pipelines for Real Robots project page / paper Exploring in language space: a VLM iteratively refines task prompts from rollout observations, enabling VLA policies to bootstrap reinforcement learning from zero-reward starts.
	Bimanual 3D Hand Motion and Articulation Forecasting in Everyday Images Aditya Prakash, Richard Li, David Forsyth, Saurabh Gupta 2025 project page / code Forecasting bimanual 3D hand motion and articulation from a single image using a diffusion-based lifting and forecasting pipeline.
	Stable Object Reorientation using Contact Plane Registration Richard Li, Carlos Esteves, Ameesh Makadia, Pulkit Agrawal ICRA 2022 project page / video / code Predicting contact points with a CVAE and plane segmentation improves object generalization and handles multimodality.
	Contact-Aware Lyapunov Controller Design via Alternating Optimization Richard Li, Timur Garipov 2022 paper / video Synthesizing Lyapunov controllers through contact with alternating optimization.
	Vision-Based Proprioceptive Sensing for Soft Robotic Fingers Richard Li, Annan Zhang 2021 paper Vision-based fingertip pose estimation with internal camera and CNN pose estimator.
	Towards Practical Multi-object Manipulation using Relational Reinforcement Learning Richard Li, Allan Jabri, Trevor Darrell, Pulkit Agrawal ICRA 2020, ICML 2020: Bridge Between Perception and Reasoning Workshop project page / video / code Multi-object, long-horizon manipulation can be autonomously learned using a curriculum and graph neural network architecture.

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