DMAP: a Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body (2209.14218v1)

Abstract: Biological and artificial agents need to deal with constant changes in the real world. We study this problem in four classical continuous control environments, augmented with morphological perturbations. Learning to locomote when the length and the thickness of different body parts vary is challenging, as the control policy is required to adapt to the morphology to successfully balance and advance the agent. We show that a control policy based on the proprioceptive state performs poorly with highly variable body configurations, while an (oracle) agent with access to a learned encoding of the perturbation performs significantly better. We introduce DMAP, a biologically-inspired, attention-based policy network architecture. DMAP combines independent proprioceptive processing, a distributed policy with individual controllers for each joint, and an attention mechanism, to dynamically gate sensory information from different body parts to different controllers. Despite not having access to the (hidden) morphology information, DMAP can be trained end-to-end in all the considered environments, overall matching or surpassing the performance of an oracle agent. Thus DMAP, implementing principles from biological motor control, provides a strong inductive bias for learning challenging sensorimotor tasks. Overall, our work corroborates the power of these principles in challenging locomotion tasks.

• The paper introduces the DMAP architecture, which enables reinforcement learning agents to adapt locomotion to changing bodies using biologically inspired independent processing, attention-based gated connectivity, and distributed control.
• Experiments show DMAP achieves competitive performance, often exceeding baselines like Oracle, demonstrating robust zero-shot adaptation to various morphological perturbations in locomotion tasks.
• DMAP's success highlights the value of biologically inspired architectures for enhancing adaptability in reinforcement learning and robotics, opening avenues for advanced autonomous systems and real-world applications.

Summary of "DMAP: A Distributed Morphological Attention Policy for Learning to Locomote with a Changing Body"

The paper introduces a novel architecture, the Distributed Morphological Attention Policy (DMAP), aimed at equipping reinforcement learning agents with the ability to adapt to morphological changes in continuous control environments. This design is motivated by principles found in biological motor control systems, emphasizing independent somatosensory processing, dynamic gating of sensory information, and distributed control among body parts.

Methodology

DMAP constructs a policy network architecture that integrates proprioceptive feedback and an attention mechanism to handle changing body morphologies. The architecture involves:

• Independent Processing: Each proprioceptive input channel is independently processed to create representations that are subsequently used in decision-making. This step draws parallels with lower-level alimentary processing in biological systems before information amalgamation.
• Gated Connectivity: A key aspect of DMAP is the attention mechanism that dynamically adjusts the importance of sensory inputs for each joint's control policy. This attention is dynamically updated based on recent sensory histories, allowing for flexible adaptation to morphological perturbations.
• Distributed Control: The architecture employs separate control networks for each joint, mirroring the decentralized nature of biological motor control and providing flexibility in handling localized changes to morphology.

Experimental Setup

The designed architecture was tested in four modified OpenAI Gym environments—Ant, Half Cheetah, Walker, and Hopper—each experiencing varied morphological perturbations. The performance of DMAP was compared against simpler models and state-of-the-art architectures, such as the Oracle and rapid motor adaptation (RMA), across different perturbation intensities.

Results

The analysis revealed several key findings:

• Comparative Performance: DMAP consistently demonstrated competitive performance, often surpassing that of the Oracle which has explicit knowledge of the morphological parameters, especially in tasks demanding robust adaptation.
• Robustness to Perturbations: Its ability to adapt without direct morphological data showcases DMAP’s robust generalization capability in zero-shot adaptation scenarios.
• Attention Dynamics: Visualization of the attention weights across episodes suggested structured rotational dynamics, aligning well with cyclic motor activities such as gaits in biological locomotion.

Implications and Future Directions

The results underscore potential advancements in autonomous robotics, where the adaptability to unforeseen changes is crucial. DMAP's architecture indicates the value of biologically inspired structures in enhancing policy learning in reinforcement learning. Further exploration could integrate this architecture with sensor integration methods for enhanced real-world applications. Moreover, investigating more complex environmental interactions can drive deeper insights into exploiting attention mechanisms for adaptive control.

In conclusion, DMAP advances the conversation on integrating biologically inspired mechanisms within machine learning frameworks, specifically highlighting the critical role of sensory processing and dynamic attention in the domain of morphological adaptation and control. This approach not only contributes to reinforcement learning but also opens avenues for interdisciplinary applications spanning neuro-inspired algorithms in AI and beyond.