available projects | Robotics Student Projects in CRL

2025

Inverse Design for Robotic Assembly via Task and Motion Planning

Yijiang Huang and Gabriele Fadini

task-and-motion-planning, robotic-assembly, inverse-design, co-design, 2025

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Can we optimize a design so that a robot can assemble it more easily? This project explores inverse design for robotic assembly using task and motion planning (TAMP), coupling design optimization with planning algorithms that lays the foundation for designing robot-friendly structures.
Trajectory Optimization and Policy Distillation for Agile Control of Hybrid Mobile Robots

Jin Cheng and Stelian Coros

bipedal-locomotion, trajectory-optimization, model-predictive-control, policy-distillation, 2025

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Hybrid mobile robots that integrate wheels and legs present a promising locomotion paradigm, combining the speed and efficiency of wheeled motion with the adaptability of legged systems. However, controlling such platforms remains challenging due to their underactuated and hybrid dynamics. This project explores a control-learning framework that leverages trajectory optimization (TO) or model predictive control (MPC) to generate high-quality motion plans while concurrently distilling a lightweight neural policy through supervised learning. The approach supports both sampling-based and gradient-based optimization methods, depending on simulator capabilities, and emphasizes real-world learning by collecting rollout data directly from hardware. The resulting policy enables efficient and robust control suitable for deployment under real-time constraints. Development and evaluation will be conducted using the LIMX Dynamics TRON1 robot, a bipedal wheeled platform with 4 DoF per leg, providing a rich testbed for advancing agile and intelligent control in hybrid robotic systems.
Robotic motion planning for active environmental manipulation

Valentin Hartmann and Yijiang Huang

motion-planning, physics-simulation, mobile-manipulation, 2025

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Robotic motion planning typically assumes static, explicitly defined environments, limiting a robot’s operational capabilities. This project explores how robots can actively modify their surroundings to dynamically extend their workspace. By integrating physics-based simulation with sampling-based motion planning techniques, we aim to enable flexible mobile manipulation in changing environments.
Strategic Humanoid Box Lifting

Jin Cheng, Yijiang Huang, and Tianxu An

reinforcement-learning, trajectory-optimization, humanoid, contact-rich-manipulation, 2025

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Manipulating large, heavy objects without convenient grasp affordances poses a significant challenge for humanoid robots. This project develops a demonstration-guided reinforcement learning framework enabling a humanoid robot to strategically lift unwieldy objects through coordinated full-body motion.
Physics-informed digital twin from video for task and motion planning of deformable objects

Yijiang Huang and Jimmy Envall

physics-informed-modeling, deformable-objects, task-and-motion-planning, simulation, 2025

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Robotic task and motion planning (TAMP) for deformable objects typically relies on predefined physics models, limiting the robot’s adaptability in real-world scenarios. This project explores how robots can autonomously construct physics-informed digital twins directly from videos and integrate the learned physics model into a TAMP framework to achieve "on-demand" manipulation of deformable objects.
Learning Agile Robotic Behaviors via Zero-Shot Reinforcement Learning

Núria Armengol Urpí and Jin Cheng

Reinforcement Learning, Agile Robotics, Zero-Shot Learning, 2025

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This project aims to enable agile and adaptive behaviors in real-world quadruped robots through zero-shot reinforcement learning (RL). Traditional RL methods often rely on fixed reward functions, limiting their generalization to novel tasks. In contrast, the forward-backward (FB) framework learns representations that allow policies to generalize across a wide range of goals without retraining. We propose to implement and evaluate the FB method on the Unitree Go2 quadruped robot, benchmarking it against alternative zero-shot RL approaches. By leveraging recent advances in exploration strategies and representation learning, this work seeks to demonstrate zero-shot capabilities on a physical robot for tasks such as novel gait synthesis, pose reaching, and motion imitation. Success in this endeavor could significantly broaden the applicability of RL to real-world robotics, laying the groundwork for flexible, reward-agnostic robotic systems.
Active Perception for Ball Catching with a Quadrupedal Manipulator

Jin Cheng and Tianxu An

Loco-manipulation, Active Perception, Reinforcement Learning, 2025

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Catching fast-moving objects presents a significant challenge for mobile manipulators due to the need for tightly coupled perception and control under dynamic conditions. This project aims to develop a unified visuomotor control policy for a quadrupedal robot equipped with an arm-mounted RGB-D camera, enabling it to perceive, track, and intercept a thrown ball using only onboard sensing. By mounting the camera on the end-effector, the robot gains the ability to adjust its viewpoint in real time, mimicking human strategies of active perception. The control policy will be trained entirely in simulation with randomized ball trajectories and perception noise models to support robust, zero-shot deployment in the real world. The resulting system will advance research at the intersection of active perception, reinforcement learning, and dynamic whole-body manipulation.
Reinforcement learning for robotic 3D bin packing

Valentin Hartmann

Reinforcement Learning, Manipulation, 2025

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Many industrial assembly tasks require high accuracy when manipulating parts, e.g., when inserting an object somewhere, such as a box that we would like to put in a bin. Here, the tolerances that are required can vary wildly. While industrial manipulators are generally accurate in a controlled setting, as soon as the environment is not as structured as we would like it to be, we need to leverage feedback policies to correct for inaccuracies in estimation, and unexpected/unmodeled effects. We are interested robotic bin packing, i.e., enabling a robot to pick boxes/parcels, and placing them in a box, where other previously placed object possibly obstruct the placement.