16 accepted papers
While recent advancements in reinforcement learning have enabled quadrupedal robots to perform non-prehensile manipulation tasks like pushing, existing methods have largely overlooked the critical challenge of obstacle avoidance. In this paper, we address this significant limitation by introducing a…
Quadruped robots have demonstrated remarkable versatility in various applications, from search and rescue to exploration. Recent advancements have shifted focus from individual robots to swarms, recognizing the potential of collaborative behaviors to achieve complex tasks beyond the capabilities of
Robust Markov Decision Processes (MDPs) offer a promising framework for computing reliable policies under model uncertainty. While policy gradient methods have gained increasing popularity in robust discounted MDPs, their application to the average-reward criterion remains largely unexplored. This p…
Safe corridor-based Trajectory Optimization (TO) presents an appealing approach for collision-free path planning of autonomous robots, because its convex formulation can guarantee global optimality. The safe corridor is constructed based on the obstacle map, however, the non-ideal perception induces…
This paper presents a collaborative quadrotor-quadruped robot system for the manipulation of a cable-towed payload. In particular, we aim to solve the challenge from the unknown dynamics of the cable-towed payload. To this end, we first propose novel dynamic models for both the quadrotor and the qua…
In this paper, we propose a reactive planning system for quadruped robots based on prescribed-time control. The navigation of the quadruped robot is fundamentally depicted as omnidirectional movements, while a feedback control law is formulated to address any deviations the robot may encounter. In p…
In this paper, we present a fully distributed framework for multiple quadruped robots in environments with obstacles. Our approach utilizes Model Predictive Control (MPC) and multi-robot consensus protocol to obtain the distributed control law. It ensures that all the robots are able to avoid obstac…
Markov decision processes (MDPs) often suffer from the sensitivity issue under model ambiguity. In recent years, robust MDPs have emerged as an effective framework to overcome this challenge. Distributionally robust MDPs extend the robust MDP framework by incorporating distributional information of…
The knowledge gradient (KG) algorithm is a popular policy for the best arm identification (BAI) problem. It is built on the simple idea of always choosing the measurement that yields the greatest expected one-step improvement in the estimate of the best mean of the arms. In this research, we show th…
Robust Markov decision processes (RMDPs) provide a promising framework for computing reliable policies in the face of model errors. Many successful reinforcement learning algorithms build on variations of policy-gradient methods, but adapting these methods to RMDPs has been challenging. As a result,…
Despite being a fundamental building block for reinforcement learning, Markov decision processes (MDPs) often suffer from ambiguity in model parameters. Robust MDPs are proposed to overcome this challenge by optimizing the worst-case performance under ambiguity. While robust MDPs can provide reliabl…
Four-legged animals are able to change their gaits adaptively for lower energy consumption. However, designing a robust controller for their robot counterparts with multi-modal locomotion remains challenging. In this paper, we present a hierarchical control framework that decomposes this challenge i…
In recent years, robust Markov decision processes (MDPs) have emerged as a prominent modeling framework for dynamic decision problems affected by uncertainty. In contrast to classical MDPs, which only account for stochasticity by modeling the dynamics through a stochastic process with a known transi…
Robust Markov decision processes (RMDPs) are a useful building block of robust reinforcement learning algorithms but can be hard to solve. This paper proposes a fast, exact algorithm for computing the Bellman operator for S-rectangular robust Markov decision processes with $L_\infty$-constrained rec…
We address the problem of computing reliable policies in reinforcement learning problems with limited data. In particular, we compute policies that achieve good returns with high confidence when deployed. This objective, known as the percentile criterion, can be optimized using Robust MDPs (RMDPs).…
We describe two efficient, and exact, algorithms for computing Bellman updates in robust Markov decision processes (MDPs). The first algorithm uses a homotopy continuation method to compute updates for L1-constrained s,a-rectangular ambiguity sets. It runs in quasi-linear time for plain L1-norms and…