Autonomous ground robots navigating unstructured off-road environments face perceptual challenges, such as sensor obscuration or failure, which can lead to inaccurate perception or navigation failures. While robot adaptation has recently gained increasing attention, self-reflective robot adaptation, where robots understand and adjust to their own sensor limitations, remains under-explored. This paper proposes a novel approach for self-reflective perceptual adaptation in order to enhance robust off-road navigation. Our approach enables a robot to identify its own perceptual difficulties and dynamically adapt in challenging environments. The key novelty is learning a modality-invariant perceptual representation that encodes shared sensor data into a compact feature space. Within this representation space, the robot's dynamics model is also learned, which enables accurate prediction of future navigation paths. Extensive experiments in off-road environments with sensor obstructions and failures demonstrate that our method significantly improves adaptive capabilities and outperforms baseline and state-of-the-art approaches. More details of this work are provided on the project website: https://hcrlab.gitlab.io/project/srpa.

Adaptive locomotion is a fundamental capability for quadruped robots, particularly in real-world scenarios when they must transport novel or out-of-distribution (O.O.D.) payloads across diverse terrains. Previous learning-based methods often tightly couple a locomotion controller's learned parameters with the adaptation process, which requires extensive pre-training or slow online updates when encountering O.O.D. payloads. To enable adaptation of quadruped locomotion to O.O.D. payloads, we propose the novel Rapid Introspective Neural Adaptation (RINA) method that rapidly compensates for differences between expected and actual joint torques caused by O.O.D. payloads. RINA introduces an adaptive residual dynamics representation that decouples the learning model's parameters from those used for adaptation. A new neural operator network is introduced to learn a set of basis functions as the learning model, which are combined using linear coefficients to predict residual dynamics. Then, these residual dynamics are used to adjust the locomotion controller's output, compensating for additional torques induced by the O.O.D. payload. During execution, the mixing coefficients can be rapidly and introspectively adapted on-the-go to generate joint torque compensations for O.O.D. payloads, while keeping the learned basis functions unchanged. Experimental results have demonstrated that our RINA approach well addresses on-the-go O.O.D. payload adaptation on varied natural terrains without collecting and retraining on additional data and outperforms baseline methods. More details of this work are provided on the project website: https://hcrlab.gitlab.io/project/rina.

This paper introduces a method, Generative Adversarial Networks for Detecting Erroneous Results (GANDER), leveraging Generative Adversarial Networks to provide online error detection in manipulation tasks for autonomous robot systems. GANDER relies on mapping input images of a trained task to a learned manifold that contains only positive task executions and outcomes. When reconstructed through this manifold, the input images from successful task executions will remain largely unchanged, while the images from a failed task will change significantly. Using this insight, GANDER enables inspection and task outcome verification capabilities using a large number of positive examples but only a small set of negative examples, thus increasing the applicability of autonomous robot systems. We detail the design of GANDER and provide results of a proof-of-concept system, establishing its efficacy in an autonomous inspection, maintenance, and repair task. GANDER produces favorable results compared to baseline approaches and is capable of correctly identifying off-nominal behavior with 91.65% accuracy in our test task. Ablation studies were also performed to quantify the amount of data ultimately needed for this approach to succeed.

Sample-based motion planning approaches, such as RRT*, have been widely adopted in robotics due to their support for high-dimensional state spaces and guarantees of completeness and optimality. This paper introduces an RRT* approach (ORRT*) that leverages opportunism to (1) find solutions quickly, (2) reduce wasted compute, and (3) improve data efficiency. The key insight of the approach is to make the most of compute when expanding the search tree by adding the last viable configurations found when connecting new nodes rather than rejecting the sampled nodes outright, allowing for more productive exploration of the space. We evaluate the proposed approach in a set of mobility and manipulator postural control domains, contrasting the performance of the opportunistic approach with state-of-the-art RRT* variants. Our analysis shows that such an approach has desirable characteristics and warrants further exploration.

The Model AI Assignments session seeks to gather and disseminate the best assignment designs of the Artificial Intelligence (AI) Education community. Recognizing that assignments form the core of student learning experience, we here present abstracts of ten AI assignments from the 2019 session that are easily adoptable, playfully engaging, and flexible for a variety of instructor needs. Assignment specifications and supporting resources may be found at http: //modelai.gettysburg.edu.