ViT-A*: Legged Robot Path Planning using Vision Transformer A*

Legged robots, particularly quadrupeds, offer promising navigation capabilities, especially in scenarios requiring traversal over diverse terrains and obstacle avoidance. This paper addresses the challenge of enabling legged robots to navigate complex environments effectively through the integration of data-driven path-planning methods. We propose an approach that utilizes differentiable planners, allowing the learning of end-to-end global plans via a neural network for commanding quadruped robots. The approach leverages 2D maps and obstacle specifications as inputs to generate a global path. To enhance the functionality of the developed neural network-based path planner, we use Vision Transformers (ViT) for map pre-processing, to enable the effective handling of larger maps. Experimental evaluations on two real robotic quadrupeds (Boston Dynamics Spot and Unitree Go1) demonstrate the effectiveness and versatility of the proposed approach in generating reliable path plans

Navigation Among Movable Obstacles via Multi-Object Pushing Into Storage Zones

With the majority of mobile robot path planning methods being focused on obstacle avoidance, this paper, studies the problem of Navigation Among Movable Obstacles (NAMO) in an unknown environment, with static (i.e., that cannot be moved by a robot) and movable (i.e., that can be moved by a robot) objects. In particular, we focus on a specific instance of the NAMO problem in which the obstacles have to be moved to predefined storage zones. To tackle this problem, we propose an online planning algorithm that allows the robot to reach the desired goal position while detecting movable objects with the objective to push them towards storage zones to shorten the planned path. Moreover, we tackle the challenging problem where an obstacle might block the movability of another one, and thus, a combined displacement plan needs to be applied. To demonstrate the new algorithm's correctness and efficiency, we report experimental results on various challenging path planning scenarios. The presented method has significantly better time performance than the baseline, while also introducing multiple novel functionalities for the NAMO problem

One-Shot Transfer of Affordance Regions? AffCorrs!

In this work, we tackle one-shot visual search of object parts. Given a single reference image of an object with annotated affordance regions, we segment semantically corresponding parts within a target scene. We propose AffCorrs, an unsupervised model that combines the properties of pre-trained DINO-ViT's image descriptors and cyclic correspondences. We use AffCorrs to find corresponding affordances both for intra- and inter-class one-shot part segmentation. This task is more difficult than supervised alternatives, but enables future work such as learning affordances via imitation and assisted teleoperation.Comment: Published in Conference on Robot Learning, 2022 For code and dataset, refer to https://sites.google.com/view/affcorr

Reinforcement Learning-based Grasping via One-Shot Affordance Localization and Zero-Shot Contrastive Language–Image Learning

We present a novel robotic grasping system using a caging-style gripper, that combines one-shot affordance localization and zero-shot object identification. We demonstrate an integrated system requiring minimal prior knowledge, focusing on flexible few-shot object agnostic approaches. For grasping a novel target object, we use as input the color and depth of the scene, an image of an object affordance similar to the target object, and an up to three-word text prompt describing the target object. We demonstrate the system using real-world grasping of objects from the YCB benchmark set, with four distractor objects cluttering the scene. Overall, our pipeline has a success rate of the affordance localization of 96%, object identification of 62.5%, and grasping of 72%. Videos are on the project website: https://sites.google.com/view/ rl-affcorrs-grasp