ReLMoGen: Integrating Motion Generation in Reinforcement Learning for Mobile Manipulation

By Fei Xia, Chengshu Li, Roberto Mart'in-Mart'in, Or Litany, Alexander Toshev, Silvio Savarese. 2021

80 citations recorded by the source corpus.

Many Reinforcement Learning (RL) approaches use joint control signals (positions, velocities, torques) as action space for continuous control tasks. We propose to lift the action space to a higher level in the form of subgoals for a motion generator (a combination of motion planner and trajectory executor). We argue that, by lifting the action space and by leveraging sampling-based motion planners, we can efficiently use RL to solve complex, long-horizon tasks that could not be solved with existing RL methods in the original action space. We propose ReLMoGen – a framework that combines a learned policy to predict subgoals and a motion generator to plan and execute the motion needed to reach these subgoals. To validate our method, we apply ReLMoGen to two types of tasks: 1) Interactive Navigation tasks, navigation problems where interactions with the environment are required to reach the destination, and 2) Mobile Manipulation tasks, manipulation tasks that require moving the robot base. These problems are challenging because they are usually long-horizon, hard to explore during training, and comprise alternating phases of navigation and interaction. Our method is benchmarked on a diverse set of seven robotics tasks in photo-realistic simulation environments. In all settings, ReLMoGen outperforms state-of-the-art RL and Hierarchical RL baselines. ReLMoGen also shows outstanding transferability between different motion generators at test time, indicating a great potential to transfer to real robots. For more information, please visit project website: http://svl.stanford.edu/projects/relmogen.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Reinforcement Learning

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Citations in the corpus

Do As I Can, Not As I Say: Grounding Language in Robotic Affordances (2022, background): “Others learn to hierarchically solve such long-horizon problems [79, 80, 12, 81, 54].”
Inner Monologue: Embodied Reasoning through Planning with Language Models (2022, background): “Others have approached the problem through hierarchical learning [45, 46, 34, 47, 48, 49, 50].”
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes (2024, background): “The effectiveness of end-to-end versus hybrid modular solutions varies by problem (82, 86, 100, 21, 200).”
Mobile ALOHA: Learning Bimanual Mobile Manipulation with Low-Cost Whole-Body Teleoperation (2024, background): “In order to tackle the exploration problem in high-dimensional state and action spaces of mobile manipulation tasks, prior works use predefined skill primitives [86, 91, 92], reinforcement learning with decomposed action spaces [38, 48, 58, 94, 101], or whole-body control objectives [36, 42, 99].”