Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding (2503.01842v2)

Abstract: Hybrid dynamical systems, which include continuous flow and discrete mode switching, can model robotics tasks like legged robot locomotion. Model-based methods usually depend on predefined gaits, while model-free approaches lack explicit mode-switching knowledge. Current methods identify discrete modes via segmentation before regressing continuous flow, but learning high-dimensional complex rigid body dynamics without trajectory labels or segmentation is a challenging open problem. This paper introduces Discrete-time Hybrid Automata Learning (DHAL), a framework to identify and execute mode-switching without trajectory segmentation or event function learning. Besides, we embedded it in reinforcement learning pipeline and incorporates a beta policy distribution and a multi-critic architecture to model contact-guided motions, exemplified by a challenging quadrupedal robot skateboard task. We validate our method through sufficient real-world tests, demonstrating robust performance and mode identification consistent with human intuition in hybrid dynamical systems.

Discrete-Time Hybrid Automata Learning for Robotics: Insights from Legged Locomotion and Skateboarding Tasks

The academic paper titled "Discrete-Time Hybrid Automata Learning: Legged Locomotion Meets Skateboarding" introduces an innovative framework known as Discrete-time Hybrid Automata Learning (DHAL), designed to tackle hybrid dynamical systems in robotic applications, particularly focusing on complex tasks such as quadrupedal legged locomotion and skateboarding. This essay provides an expert analysis of the methodological advancements, theoretical implications, and practical applications presented in the paper.

Overview of DHAL Framework

DHAL is positioned to address the deficiencies in both model-based and model-free approaches when dealing with hybrid dynamical systems characterized by both continuous and discrete dynamics. The authors argue that traditional model-based techniques are hindered by their reliance on predefined dynamics and the combinatorial complexity in high-dimensional spaces, while model-free reinforcement learning lacks proper mechanisms for explicitly handling mode-switching dynamics, often leading to inefficient learning.

The DHAL framework proposes a novel solution by incorporating a multi-critic architecture and leveraging a beta policy distribution. This design paradigm is illustrated through a challenging task involving a quadrupedal robot tasked with skateboarding. By abstaining from traditional trajectory segmentation and event function learning, the DHAL architecture emphasizes a discrete hybrid automata approach to efficiently handle the abrupt transitions typical in contact-rich activities.

Methodological Innovations

1. Discrete Hybrid Automata Framework: This component serves as the core of DHAL, establishing a discrete mode selector that dynamically determines the mode of operation, bypassing the need for explicit trajectory segmentation. The use of a beta distribution instead of Gaussian enables bounded action spaces, which proves particularly advantageous under conditions requiring exploration with strict constraints.
2. Multi-Critic Reinforcement Learning: The incorporation of multiple critics, each evaluating distinct aspects of the task (e.g., gliding versus pushing in skateboarding), facilitates more nuanced policy learning. This approach effectively balances exploration-exploitation, preventing entire reward signals from being dominated by dense feedback, thereby ensuring that critical sparse rewards guide policy optimization.
3. Sim2Real Transition via Underactuated Tasks: The paper provides compelling evidence of sim-to-real transfer where the learned policies in simulated environments are deployed in real-world settings without significant loss of performance. This capability marks a crucial advancement given the inherent differences between simulated and physical systems.

Numerical Results and Strong Claims

The paper underscores the robustness of the DHAL framework with detailed numerical results, demonstrating its effectiveness over existing methods. For instance, the capability to generalize behavior across different terrains, including ceramics and carpets, and under disturbances, notably outperforms prior approaches as evidenced by a success rate of up to 100% in varied scenarios. Additionally, the integration of multi-critic advantage underscores the reliability and balance of policies in handling complex tasks with sparse rewards.

Implications and Future Directions

From a theoretical standpoint, DHAL represents a significant step towards enabling robotic systems to efficiently navigate environments characterized by hybrid dynamics without relying on intricate models of each mode. By simplifying the process of mode selection and transition dynamics, DHAL stands to impact a broad array of robotic disciplines.

Practically, the implications of this research extend to various application domains where contact-driven interactions dominate, including automated logistics, rescue operations, and complex mobility situations. The paper marks a pivotal achievement in realizing robust robotic autonomy capable of dynamic adaptation.

As artificial intelligence and robotics continue to evolve, future developments may explore expanding DHAL to other domains requiring intricate task negotiation and actions necessitating complex manipulation, building upon the groundwork established in this paper. Additionally, addressing perception challenges and enhancing the expressivity of learned behaviors in real-world unstructured environments remain promising avenues for subsequent investigations. The integration of large-scale, data-driven models with the DHAL approach could further enhance adaptability and robustness in future robotic systems.