12 accepted papers
Deformable substrates such as sand and mud present significant challenges for terrestrial robots due to complex robot-terrain interactions. Inspired by mudskippers, amphibious animals that naturally adjust their tail morphology and movement jointly to navigate such environments, we investigate how t…
The capability of effectively moving on complex terrains such as sand and gravel can empower our robots to robustly operate in outdoor environments, and assist with critical tasks such as environment monitoring, search-and-rescue, and supply delivery. Inspired by the Mount Lyell salamander's ability
In this paper, we propose a new model to learn Adaptive Kernel-based Representations (AKBR) for graph classification. Unlike state-of-the-art R-convolution graph kernels that are defined by merely counting any pair of isomorphic substructures between graphs and cannot provide an end-to-end learning
Muddy terrains present significant challenges for terrestrial robots, as subtle changes in composition and water content can lead to large variations in substrate strength and force responses, causing robot to slip or stuck. This paper presents a method to estimate mud properties using proprioceptiv…
Graph-based representations are powerful tools for analyzing structured data. In this paper, we propose a novel model to learn Deep Hierarchical Attention-based Kernelized Representations (DHAKR) for graph classification. To this end, we commence by learning an assignment matrix to hierarchically ma…
The over-smoothing has emerged as a major challenge in the development of Graph Neural Networks (GNNs). While existing state-of-the-art methods effectively mitigate the diminishing distance between nodes and improve the performance of node classification, they tend to be elusive for graph-level task
Legged robots have the potential to leverage obstacles to climb steep sand slopes. However, efficiently repositioning these obstacles to desired locations is challenging. Here we present DiffusiveGRAIN, a learning-based method that enables a multi-legged robot to strategically induce localized sand…
Legged robot locomotion on sand slopes is challenging due to the complex dynamics of granular media and how the lack of solid surfaces can hinder locomotion. A promising strategy, inspired by ghost crabs and other organisms in nature, is to strategically interact with rocks, debris, and other obstac…
Moving on natural muddy terrains, where soil composition and water content vary significantly, is complex and challenging. To understand how mud properties and robot-mud interaction strategies affect locomotion performance on mud, we study the terrestrial locomotion of a mudskipper-inspired robot on
Existing work in legged robot navigation in cluttered environments often seeks collision-free paths that avoid obstacle interactions. Here we present a new approach for multi-legged robots to utilize leg-obstacle collisions to generate desired dynamics. To predict the change of robot state under rep
We study a quadrupedal robot traversing a structured (i.e., periodically spaced) obstacle field driven by an open-loop quasi-static trotting walk. Despite complex, repeated collisions and slippage between robot legs and obstacles, the robot's horizontal plane body orientation (yaw) trajectory can co
Natural substrates are often composed of particulates of varying size, from fine sand to pebbles and boulders. Robot locomotion on such heterogeneous substrates is complicated in part due to large force and kinematic fluctuations introduced by heterogeneities. To systematically explore how heterogen…