projects | Jin Cheng

2024

Meta-Reinforcement Learning for Universal Quadrupedal Locomotion Control

Fabrizio Di Giuro, Fatemeh Zargarbashi, Jin Cheng, Dongho Kang, Bhavya Sukhija, and 1 more author

In submission, 2024

Abs arXiv video

This work presents a deep reinforcement learning-based approach to develop a policy for robot-agnostic locomotion control. Our method involves training an agent equipped with memory, implemented as a recurrent policy, on a diverse set of procedurally generated quadruped robots. We demonstrate that the policies trained by our framework transfer seamlessly to both simulated and real-world quadrupeds not encountered during training, maintaining high-quality motion across platforms. Through a series of simulation and hardware experiments, we highlight the critical role of the recurrent unit in enabling generalization, rapid adaptation to changes in the robot’s dynamic properties, and sample efficiency.
RobotKeyframing: Learning Locomotion with High-Level Objectives via Mixture of Dense and Sparse Rewards

Fatemeh Zargarbashi, Jin Cheng, Dongho Kang, Robert Sumner, and Stelian Coros

In submission, 2024

Abs arXiv

This paper presents a novel learning-based control framework that uses keyframing to incorporate high-level objectives in natural locomotion for legged robots. These high-level objectives are specified as a variable number of partial or complete pose targets that are spaced arbitrarily in time. Our proposed framework utilizes a multi-critic reinforcement learning algorithm to effectively handle the mixture of dense and sparse rewards. Additionally, it employs a transformer-based encoder to accommodate a variable number of input targets, each associated with specific time-to-arrivals. Throughout simulation and hardware experiments, we demonstrate that our framework can effectively satisfy the target keyframe sequence at the required times. In the experiments, the multi-critic method significantly reduces the effort of hyperparameter tuning compared to the standard single-critic alternative. Moreover, the proposed transformer-based architecture enables robots to anticipate future goals, which results in quantitative improvements in their ability to reach their targets.
Offline Diversity Maximization Under Imitation Constraints

Marin Vlastelica, Jin Cheng, Georg Martius, and Pavel Kolev

2024 Reinforcement Learning Conference (RLC 2024), 2024

Abs arXiv Website

There has been significant recent progress in the area of unsupervised skill discovery, utilizing various information-theoretic objectives as measures of diversity. Despite these advances, challenges remain: current methods require significant online interaction, fail to leverage vast amounts of available task-agnostic data and typically lack a quantitative measure of skill utility. We address these challenges by proposing a principled offline algorithm for unsupervised skill discovery that, in addition to maximizing diversity, ensures that each learned skill imitates state-only expert demonstrations to a certain degree. Our main analytical contribution is to connect Fenchel duality, reinforcement learning, and unsupervised skill discovery to maximize a mutual information objective subject to KL-divergence state occupancy constraints. Furthermore, we demonstrate the effectiveness of our method on the standard offline benchmark D4RL and on a custom offline dataset collected from a 12-DoF quadruped robot for which the policies trained in simulation transfer well to the real robotic system.
Learning Diverse Skills for Local Navigation under Multi-constraint Optimality

Jin Cheng, Marin Vlastelica, Pavel Kolev, Chenhao Li, and Georg Martius

2024 IEEE International Conference on Robotics and Automation (ICRA 2024), 2024

Abs arXiv video Website

Despite many successful applications of data-driven control in robotics, extracting meaningful diverse behaviors remains a challenge. Typically, task performance needs to be compromised in order to achieve diversity. In many scenarios, task requirements are specified as a multitude of reward terms, each requiring a different trade-off. In this work, we take a constrained optimization viewpoint on the quality-diversity trade-off and show that we can obtain diverse policies while imposing constraints on their value functions which are defined through distinct rewards. In line with previous work, further control of the diversity level can be achieved through an attract-repel reward term motivated by the Van der Waals force. We demonstrate the effectiveness of our method on a local navigation task where a quadruped robot needs to reach the target within a finite horizon. Finally, our trained policies transfer well to the real 12-DoF quadruped robot, Solo12, and exhibit diverse agile behaviors with successful obstacle traversal.

2023

RL+ Model-based Control: Using On-demand Optimal Control to Learn Versatile Legged Locomotion

Dongho Kang, Jin Cheng, Miguel Zamora, Fatemeh Zargarbashi, and Stelian Coros

IEEE Robotics and Automation Letters (RA-L), 2023

Abs arXiv video HTML Website

This letter presents a versatile control method for dynamic and robust legged locomotion that integrates model-based optimal control with reinforcement learning (RL). Our approach involves training an RL policy to imitate reference motions generated on-demand through solving a finite-horizon optimal control problem. This integration enables the policy to leverage human expertise in generating motions to imitate while also allowing it to generalize to more complex scenarios that require a more complex dynamics model. Our method successfully learns control policies capable of generating diverse quadrupedal gait patterns and maintaining stability against unexpected external perturbations in both simulation and hardware experiments. Furthermore, we demonstrate the adaptability of our method to more complex locomotion tasks on uneven terrain without the need for excessive reward shaping or hyperparameter tuning.

2022

Haptic Teleoperation of High-dimensional Robotic Systems Using a Feedback MPC Framework

Jin Cheng, Firas Abi-Farraj, Farbod Farshidian, and Marco Hutter

2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2022), 2022

Abs arXiv video HTML

Model Predictive Control (MPC) schemes have proven their efficiency in controlling high degree-of-freedom (DoF) complex robotic systems. However, they come at a high computational cost and an update rate of about tens of hertz. This relatively slow update rate hinders the possibility of stable haptic teleoperation of such systems since the slow feedback loops can cause instabilities and loss of transparency to the operator. This work presents a novel framework for transparent teleoperation of MPC-controlled complex robotic systems. In particular, we employ a feedback MPC approach [1] and exploit its structure to account for the operator input at a fast rate which is independent of the update rate of the MPC loop itself. We demonstrate our framework on a mobile manipulator platform and show that it significantly improves haptic teleoperation’s transparency and stability. We also highlight that the proposed feedback structure is constraint satisfactory and does not violate any constraints defined in the optimal control problem. To the best of our knowledge, this work is the first realization of the bilateral teleoperation of a legged manipulator using a whole-body MPC framework.