20 accepted papers
Optimizing Conditional Value-at-risk (CVaR) using policy gradient (a.k.a CVaR-PG) faces significant challenges of sample inefficiency. This inefficiency stems from the fact that it focuses on tail-end performance and overlooks many sampled trajectories. We address this problem by augmenting CVaR wit…
Double-Deck Multi-Agent Pickup and Delivery (DD-MAPD) models the multi-robot shelf rearrangement problem in automated warehouses. MAPF-DECOMP is a recent framework that first computes collision-free shelf trajectories with a MAPF solver and then assigns agents to execute them. While efficient, it en
Transformable wheel-legged robots can adjust their configuration according to terrain conditions, enabling effective operation in harsh environments. While existing controllers based on preset commands have successfully demonstrated the feasibility of reconfigurable mechanisms, they still struggle t…
Morphing quadrotors offer enhanced maneuverability and adaptability in confined spaces, while their structural variations pose challenges to trajectory planning and control. This paper presents a time-optimal trajectory planning and model predictive control framework for the morphing quadrotor. The …
High maneuverability is essential to the autonomous operation of underwater robots. To achieve real-time maneuvering motion, the control strategy must take into account nonlinear hydrodynamic effects, which are extremely difficult to accurately capture during motion and therefore a balance must be s
In logistics applications, the vision-based technology for grasping target objects in the air is relatively mature. However, when operating across the air and water such as grasping marine products from the water, the visual information collected by the camera will be disturbed by ripples and bubble
Current multi-modal hybrid robots with flight and wheeled modes have fallen into the dilemma that they can only avoid obstacles by re-taking off when encountering obstacles due to the poor performance of wheeled obstacle-crossing. To tackle this problem, this paper presents a novel multi-modal hybri…
Morphing quadrotors that can be potentially applied to confined spaces such as warehouses, tanks, and pipelines have flourished in recent years. Most work has focused on the mechanical feasibility of the morphing systems and high-level flight controller design, with limited discussions on low-level…
Due to the low motion efficiency and maneuver-ability of underwater robots with six degrees of freedom, it is challenging for them to respond quickly to the attitude requirements during underwater autonomous manipulation. This paper presents a novel autonomous underwater robot with fully vectored pr…
Restricting the variance of a policy’s return is a popular choice in risk-averse Reinforcement Learning (RL) due to its clear mathematical definition and easy interpretability. Traditional methods directly restrict the total return variance. Recent methods restrict the per-step reward variance as a…
When deploying Reinforcement Learning (RL) agents into a physical system, we must ensure that these agents are well aware of the underlying constraints. In many real-world problems, however, the constraints are often hard to specify mathematically and unknown to the RL agents. To tackle these issues…
Distributional Reinforcement Learning (RL) differs from traditional RL by estimating the distribution over returns to capture the intrinsic uncertainty of MDPs. One key challenge in distributional RL lies in how to parameterize the quantile function when minimizing the Wasserstein metric of temporal…
Learning communication via deep reinforcement learning (RL) or imitation learning (IL) has recently been shown to be an effective way to solve Multi-Agent Path Finding (MAPF). However, existing communication based MAPF solvers focus on broadcast communication, where an agent broadcasts its message t
A major task of sports analytics is player evaluation. Previous methods commonly measured the impact of players' actions on desirable outcomes (e.g., goals or winning) without considering the risk induced by stochastic game dynamics. In this paper, we design an uncertainty-aware Reinforcement Learn…
Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems. Recent methods have applied reinforcement learning (RL) to learn decentralized polices in partially observable environments. A fundamental challenge of obtaining collision-free policy is that agents need to learn co-operati…
A major task of sports analytics is to rank players based on the impact of their actions. Recent methods have applied reinforcement learning (RL) to assess the value of actions from a learned action value or Q-function. A fundamental challenge for estimating action values is that explicit reward sig…
Inspired by the inchworm locomotion mechanism, this paper presents our recently developed self-deformable capsule-like robot. The robot has the actuated deformation capability that relies on a novel rigid elements-based morphing structure (REMS) and its soft actuation mechanisms. When the robot defo…
The paper presents the development of a novel deformable quad-rotor enabled aerial gripper. The mechanism of our deformable quad-rotor is based on simultaneous expansion or contraction of the quad-rotor body, which is generated by controlling a rigid elements based morphing structure (REMS). Such de…
Inspired by natural earthworm locomotion behavior and segmental muscle motion mechanism, this paper presents our recently developed compliant modular earthwormlike robot with the novel segmental muscle-mimetic design unit that is capable of efficiently mimicking earthworms' segmental muscle contract…
To improve the obstacle surmounting performance of the quad-rotor vehicle, this paper focuses on designing, kinematically and dynamically characterizing a novel deformable quad-rotor that is based on the scissor-like foldable structures. The foldable structure allows that the volume of the quad-roto…