Causal Policy Gradient for Whole-Body Mobile Manipulation
- URL: http://arxiv.org/abs/2305.04866v4
- Date: Thu, 28 Sep 2023 16:17:08 GMT
- Title: Causal Policy Gradient for Whole-Body Mobile Manipulation
- Authors: Jiaheng Hu, Peter Stone, Roberto Mart\'in-Mart\'in
- Abstract summary: We introduce Causal MoMa, a new reinforcement learning framework to train policies for typical MoMa tasks.
We evaluate the performance of Causal MoMa on three types of simulated robots across different MoMa tasks.
- Score: 39.3461626518495
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Developing the next generation of household robot helpers requires combining
locomotion and interaction capabilities, which is generally referred to as
mobile manipulation (MoMa). MoMa tasks are difficult due to the large action
space of the robot and the common multi-objective nature of the task, e.g.,
efficiently reaching a goal while avoiding obstacles. Current approaches often
segregate tasks into navigation without manipulation and stationary
manipulation without locomotion by manually matching parts of the action space
to MoMa sub-objectives (e.g. learning base actions for locomotion objectives
and learning arm actions for manipulation). This solution prevents simultaneous
combinations of locomotion and interaction degrees of freedom and requires
human domain knowledge for both partitioning the action space and matching the
action parts to the sub-objectives. In this paper, we introduce Causal MoMa, a
new reinforcement learning framework to train policies for typical MoMa tasks
that makes use of the most favorable subspace of the robot's action space to
address each sub-objective. Causal MoMa automatically discovers the causal
dependencies between actions and terms of the reward function and exploits
these dependencies through causal policy gradient that reduces gradient
variance compared to previous state-of-the-art reinforcement learning
algorithms, improving convergence and results. We evaluate the performance of
Causal MoMa on three types of simulated robots across different MoMa tasks and
demonstrate success in transferring the policies trained in simulation directly
to a real robot, where our agent is able to follow moving goals and react to
dynamic obstacles while simultaneously and synergistically controlling the
whole-body: base, arm, and head. More information at
https://sites.google.com/view/causal-moma.
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