arXiv subjects

Adam Imdieke

Publications and source records attributed to Adam Imdieke.

3 recordsLinked to original sources

arXiv2025

SPARK-Remote: A Cost-Effective System for Remote Bimanual Robot Teleoperation

Robot teleoperation enables human control over robotic systems in environments where full autonomy is challenging. Recent advancements in low-cost teleoperation devices and VR/AR technologies have expanded accessibility, particularly for bimanual robot manipulators. However, transitioning from in-person to remote teleoperation presents challenges in task performance. We introduce SPARK, a kinematically scaled, low-cost teleoperation system for operating bimanual robots. Its effectiveness is compared to existing technologies like the 3D SpaceMouse and VR/AR controllers. We further extend SPARK to SPARK-Remote, integrating sensor-based force feedback using haptic gloves and a force controller for remote teleoperation. We evaluate SPARK and SPARK-Remote variants on 5 bimanual manipulation tasks which feature operational properties - positional precision, rotational precision, large movements in the workspace, and bimanual collaboration - to test the effective teleoperation modes. Our findings offer insights into improving low-cost teleoperation interfaces for real-world applications. For supplementary materials, additional experiments, and qualitative results, visit the project webpage: https://bit.ly/41EfcJa

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arXiv2024

AugInsert: Learning Robust Visual-Force Policies via Data Augmentation for Object Assembly Tasks

Operating in unstructured environments like households requires robotic policies that are robust to out-of-distribution conditions. Although much work has been done in evaluating robustness for visuomotor policies, the robustness evaluation of a multisensory approach that includes force-torque sensing remains largely unexplored. This work introduces a novel, factor-based evaluation framework with the goal of assessing the robustness of multisensory policies in a peg-in-hole assembly task. To this end, we develop a multisensory policy framework utilizing the Perceiver IO architecture to learn the task. We investigate which factors pose the greatest generalization challenges in object assembly and explore a simple multisensory data augmentation technique to enhance out-of-distribution performance. We provide a simulation environment enabling controlled evaluation of these factors. Our results reveal that multisensory variations such as Grasp Pose present the most significant challenges for robustness, and naive unisensory data augmentation applied independently to each sensory modality proves insufficient to overcome them. Additionally, we find force-torque sensing to be the most informative modality for our contact-rich assembly task, with vision being the least informative. Finally, we briefly discuss supporting real-world experimental results. For additional experiments and qualitative results, we refer to the project webpage https://rpm-lab-umn.github.io/auginsert/ .

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arXiv2024

Talk Through It: End User Directed Manipulation Learning

Training generalist robot agents is an immensely difficult feat due to the requirement to perform a huge range of tasks in many different environments. We propose selectively training robots based on end-user preferences instead. Given a factory model that lets an end user instruct a robot to perform lower-level actions (e.g. 'Move left'), we show that end users can collect demonstrations using language to train their home model for higher-level tasks specific to their needs (e.g. 'Open the top drawer and put the block inside'). We demonstrate this hierarchical robot learning framework on robot manipulation tasks using RLBench environments. Our method results in a 16% improvement in skill success rates compared to a baseline method. In further experiments, we explore the use of the large vision-language model (VLM), Bard, to automatically break down tasks into sequences of lower-level instructions, aiming to bypass end-user involvement. The VLM is unable to break tasks down to our lowest level, but does achieve good results breaking high-level tasks into mid-level skills. We have a supplemental video and additional results at talk-through-it.github.io.

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