More Like this

1. Examining Postural Responses to Perturbations in 3D: A Pilot Study

   https://doi.org/10.1109/ICORR66766.2025.11063047  

   Hailey, Rhet O; Rose, Chad G (May 2025, IEEE)

   Affecting muscle spasticity, strength, and coordination, stroke results in alterations to muscle control and ability to compensate from unexpected perturbations. Post-stroke, upper extremity movements are heavily modified from perturbations, which increase the difficulty of activities of daily living (ADLs). Postural responses from upper extremity perturbations in healthy and stroke populations have been examined in movements constrained to 2D planar motion, and may provide insight as an assessment tool to help inform therapists to better structure rehabilitation training regimens towards individualized health care for improved long-term outcomes. However, implications on constraining motion in the horizontal plane are not clear and may reduce the generalizability of the findings to the movement through unconstrained 3D space necessary for ADLs. In this paper, we explore the effects of joint perturbations on the elbow and shoulder in unconstrained, gravity-compensated position holding tasks. We present a metric-diverse, dynamic task framework building upon previous 2D experiments designed to better assess rehabilitative efforts in movement trajectories with applied gravity compensation in three dimensional space aimed towards the generalizability of 3D motion. Results suggest that motion of multi-DoF joints display varied movement qualities in 3D space with robotic gravity compensation when compared to constrained planar movements. 

   more » « less
2. Algorithms for shaping a particle swarm with a shared input by exploiting non-slip wall contacts

   https://doi.org/10.1109/IROS.2017.8206294  

   Shahrokhi, Shiva; Mahadev, Arun; Becker, Aaron T. (September 2017, Intelligent Robots and Systems (IROS), 2017 IEEE/RSJ International Conference on)

   There are driving applications for large populations of tiny robots in robotics, biology, and chemistry. These robots often lack onboard computation, actuation, and communication. Instead, these “robots” are particles carrying some payload and the particle swarm is controlled by a shared control input such as a uniform magnetic gradient or electric field. In previous works, we showed that the 2D position of each particle in such a swarm is controllable if the workspace contains a single obstacle the size of one particle. Requiring a small, rigid obstacle suspended in the middle of the workspace is a strong constraint, especially in 3D. This paper relaxes that constraint, and provides position control algorithms that only require non-slip wall contact in 2D. Both in vivo and artificial environments often have such boundaries. We assume that particles in contact with the boundaries have zero velocity if the shared control input pushes the particle into the wall. This paper provides a shortest-path algorithm for positioning a two-particle swarm, and a generalization to positioning an n-particle swarm. Results are validated with simulations and a hardware demonstration. 

   more » « less
3. Learning neural representation of camera pose with matrix representation of pose shift via view synthesis

   Zhu, Y.; Gao, R.; Huang, S.; Zhu, S. C.; Wu, Y. N. (January 2021, IEEE Conference on Computer Vision and Pattern Recognition)

   How to effectively represent camera pose is an essential problem in 3D computer vision, especially in tasks such as camera pose regression and novel view synthesis. Traditionally, 3D position of the camera is represented by Cartesian coordinate and the orientation is represented by Euler angle or quaternions. These representations are manually designed, which may not be the most effective representation for downstream tasks. In this work, we propose an approach to learn neural representations of camera poses and 3D scenes, coupled with neural representations of local camera movements. Specifically, the camera pose and 3D scene are represented as vectors and the local camera movement is represented as a matrix operating on the vector of the camera pose. We demonstrate that the camera movement can further be parametrized by a matrix Lie algebra that underlies a rotation system in the neural space. The vector representations are then concatenated and generate the posed 2D image through a decoder network. The model is learned from only posed 2D images and corresponding camera poses, without access to depths or shapes. We conduct extensive experiments on synthetic and real datasets. The results show that compared with other camera pose representations, our learned representation is more robust to noise in novel view synthesis and more effective in camera pose regression. 

   more » « less
4. Trajectory generation on SE(3) with applications to a class of underactuated vehicles

   https://doi.org/10.1109/CDC.2017.8264029  

   Dhullipalla, Mani H.; Hamrah, Reza; Sanyal, Amit K. (December 2017, 2017 IEEE 56th Annual Conference on Decision and Control (CDC))

   This paper addresses the problem of generating a continuous and differentiable trajectory on the Lie group of rigid body motions, SE(3), for a class of underactuated vehicles modeled as rigid bodies. The three rotational degrees of freedom (DOF) are independently actuated, while only one translational DOF is actuated by a body-fixed thrust vector. This model is applicable to a large set of unmanned vehicles, including fixed-wing and rotorcraft unmanned aerial vehicles (UAVs). The formulation utilizes exponential coordinates to express the underactuation constraint as an intrinsic part of the problem. It provides steps to generate a rest-to-rest trajectory after obtaining conditions that guarantee controllability. An attitude trajectory is selected to satisfy the given initial and final attitude state. The position trajectory generation is subsequently posed as an optimal control problem expressed as a linear quadratic regulator (LQR) in the exponential coordinates corresponding to position. As a result, an optimal position trajectory is obtained which ensures that the trajectory generated is feasible with realistic velocities and with given initial pose and final pose, while satisfying the underactuation constraint. Numerical simulation results are obtained that validate this trajectory generation scheme. 

   more » « less
5. Residual Policy Learning for Shared Autonomy

   Schaff, Charles; Walter, Matthew R. (July 2020, Proceedings of Robotics: Science and Systems (RSS))

   Shared autonomy provides an effective framework for human-robot collaboration that takes advantage of the complementary strengths of humans and robots to achieve common goals. Many existing approaches to shared autonomy make restrictive assumptions that the goal space, environment dynamics, or human policy are known a priori, or are limited to discrete action spaces, preventing those methods from scaling to complicated real world environments. We propose a model-free, residual policy learning algorithm for shared autonomy that alleviates the need for these assumptions. Our agents are trained to minimally adjust the human’s actions such that a set of goal-agnostic constraints are satisfied. We test our method in two continuous control environments: Lunar Lander, a 2D flight control domain, and a 6-DOF quadrotor reaching task. In experiments with human and surrogate pilots, our method significantly improves task performance without any knowledge of the human’s goal beyond the constraints. These results highlight the ability of model-free deep reinforcement learning to realize assistive agents suited to continuous control settings with little knowledge of user intent. 

   more » « less