Enhancing Navigation Efficiency of Quadruped Robots via Leveraging Personal Transportation Platforms

TL;DR

The paper tackles the challenge of limited long-range efficiency in quadruped robots by enabling active transporter riding (RL-ATR). It casts the problem as a POMDP and introduces a learning-based framework with a transporter-riding policy and two estimators, augmented by a grid adaptive curriculum to cover velocity commands $P(oldsymbol{c}_{v,oldsymbol{oldsymbol{ extomega}}})$. Through extensive simulation across transporter designs and robot platforms, RL-ATR achieves proficient command tracking and reduced Cost of Transport (CoT) compared to legged locomotion, with ablations quantifying the contributions of curriculum and estimators. The work broadens quadruped locomotion modalities, potentially extending operational range and energy efficiency in real-world scenarios, and sets the stage for future real-world experiments including mounting/dismounting and exteroceptive sensing.

Abstract

Quadruped robots face limitations in long-range navigation efficiency due to their reliance on legs. To ameliorate the limitations, we introduce a Reinforcement Learning-based Active Transporter Riding method (\textit{RL-ATR}), inspired by humans' utilization of personal transporters, including Segways. The \textit{RL-ATR} features a transporter riding policy and two state estimators. The policy devises adequate maneuvering strategies according to transporter-specific control dynamics, while the estimators resolve sensor ambiguities in non-inertial frames by inferring unobservable robot and transporter states. Comprehensive evaluations in simulation validate proficient command tracking abilities across various transporter-robot models and reduced energy consumption compared to legged locomotion. Moreover, we conduct ablation studies to quantify individual component contributions within the \textit{RL-ATR}. This riding ability could broaden the locomotion modalities of quadruped robots, potentially expanding the operational range and efficiency.

Figures (7)

• Figure 1: Demonstration of the RL-ATR: Quadruped robots utilizing personal transportation platforms (transporters) with adept riding ability for efficient long-range navigation. Specific transporter dynamics are detailed in Sec. \ref{['sec:tp_types']}.
• Figure 2: This figure illustrates the concept of transporter riding tasks involving two types of transporters. Additionally, it introduces some variable notations, such as the coordinate frames for the robot body ($\mathcal{B}$), platform ($\mathcal{P}$), and world ($\mathcal{W}$); entities for the robot body ($B$) and several platforms ($P$, $P_R$, $P_L$); and the foot contact forces ($\mathbf{f}_c$) along with their relative positions ($\mathbf{r}_c$).
• Figure 3: Overall Framework of the Reinforcement Learning-based Active Transporter Riding Method (RL-ATR). This integrates four key modules for developing a transporter riding policy $\pi_\theta$: (1) simulation environments modeling transporter and robot dynamics; (2) a command scheduling method that systematically raises the riding-task difficulty for effective policy learning; (3) a policy optimization algorithm; and (4) an active transporter riding policy with estimators. Components used in the training phase are highlighted in red, those in the deployment phase in yellow, and in both phases in both colors.
• Figure 4: Heatmaps of tracking errors for $c_v$ (forward velocity) and $c_\omega$ (yaw rate) commands on the $\mathcal{C}^{\text{eval}}_{v, \omega}$, with corresponding command area graphs margolis2024rapid.
• Figure 5: Long-range Navigation Efficiency Analysis. (a) Two experimental scenarios, with yellow dotted lines illustrating representative planned paths. (b) Distributions of the mechanical Cost of Transport (CoT) bjelonic2018skating for legged locomotion margolis2024rapid and riding approaches using two types of transporters.