A major appeal of learning from demonstrations or imitation learning (IL) in robotics is that it learns a policy directly from lay users. However, Lay users may inadvertently provide erroneous demonstrations that lead to learning of policies that are inaccurate and hence, unsafe for humans and/or robot. This paper makes two contributions in the endeavour of recognizing human errors in demonstrations and thereby helping to learn a safe IL policy. First, we created a dataset – Layman V1.0 – with 15 lay users who provided a total of 1200 demonstrations for three simulated tasks – Lift, Can and Square in the simulated Robosuite environment – and two real robot tasks with a Sawyer robot, using a custom designed Android app for tele-operation. Second, we propose a framework named Behavior Cloning for Error Detection (BED) to autonomously detect and discard erroneous demonstrations from a demonstration pool. Our method uses a Behavior Cloning method as self-supervised technique and assigns binary weight to each demonstration based on its inconsistencies with the rest of the demonstrations. We show the effectiveness of this framework in detecting incorrect demonstrations in the Layman V1.0 dataset. We further show that state-of-the-art (SOTA) policy learners learns a better policy when bad demonstrations, identified through the proposed framework, are removed from the training pool. Dataset and Codes are available in https://github.com/AssistiveRoboticsUNH/bed

Blind users rely on keyboards and assistive technologies like screen readers to interact with user interface (UI) elements. In modern applications with complex UI hierarchies, navigating to different UI elements poses a significant accessibility challenge. Users must listen to screen reader audio descriptions and press relevant keyboard keys one at a time. This paper introduces Wheeler, a novel three-wheeled, mouse-shaped stationary input device, to address this issue. Informed by participatory sessions, Wheeler enables blind users to navigate up to three hierarchical levels in an app independently using three wheels instead of navigating just one level at a time using a keyboard. The three wheels also offer versatility, allowing users to repurpose them for other tasks, such as 2D cursor manipulation. A study with 12 blind users indicates a significant reduction (40%) in navigation time compared to using a keyboard. Further, a diary study with our blind co-author highlights Wheeler’s additional benefits, such as accessing UI elements with partial metadata and facilitating mixed-ability collaboration.

In this paper, we propose a novel network architecture for visual imitation learning that exploits neural radiance fields (NeRFs) and key-point correspondence for self-supervised visual motor policy learning. The proposed network architecture incorporates a dynamic system output layer for policy learning. Combining the stability and goal adaption properties of dynamic systems with the robustness of keypoint-based correspondence yields a policy that is invariant to significant clutter, occlusions, lighting conditions changes, and spatial variations in goal configurations. Experiments on multiple manipulation tasks show that our method outperforms comparable visual motor policy learning methods on both in-distribution and out-of-distribution scenarios when using a small number of training samples.