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1. Telerobot operators can account for varying transmission dynamics in a visuo-haptic object tracking task

   https://doi.org/10.1109/ICRA48891.2023.10160509  

   Singhala, Mohit; Brown, Jeremy D. (May 2023, 2023 IEEE International Conference on Robotics and Automation (ICRA))

   Humans possess an innate ability to incorporate tools into our body schema to perform a myriad of tasks not possible with our natural limbs. Human-in-the-loop telerobotic systems (HiLTS) are tools that extend human manipulation capabilities to remote and virtual environments. Unlike most hand-held tools, however, HiLTS often possess complex electromechanical architectures that introduce non-trivial transmission dynamics between the robot’s leader and follower, which alter or obfuscate the environment’s dynamics. While considerable research has focused on negating or circumventing these dynamics, it is not well understood how capable human operators are at incorporating these transmission dynamics into their sensorimotor control scheme. To begin answering this question, we recruited N=12 participants to use a novel reconfigurable teleoperator with varying transmission dynamics to perform a visuo-haptic tracking task. Contrary to our original hypothesis, our findings demonstrate that humans can account for substantial differences in teleoperator transmission dynamics and produce the compensatory strategies necessary to adequately control the teleoperator. These findings suggest that advances in transparency algorithms and haptic feedback approaches must be coupled with control designs that leverage the unique capabilities of the human operator in the loop. 

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2. Learning Environment Constraints in Collaborative Robotics: A Decentralized Leader-Follower Approach

   https://doi.org/10.1109/IROS51168.2021.9636444  

   Bujarbaruah, Monimoy; Sturz, Yvonne R.; Holda, Conrad; Johansson, Karl H.; Borrelli, Francesco (September 2021, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   In this paper, we propose a leader-follower hierarchical strategy for two robots collaboratively transporting an object in a partially known environment with obstacles. Both robots sense the local surrounding environment and react to obstacles in their proximity. We consider no explicit communication, so the local environment information and the control actions are not shared between the robots. At any given time step, the leader solves a model predictive control (MPC) problem with its known set of obstacles and plans a feasible trajectory to complete the task. The follower estimates the inputs of the leader and uses a policy to assist the leader while reacting to obstacles in its proximity. The leader infers obstacles in the follower’s vicinity by using the difference between the predicted and the real-time estimated follower control action. A method to switch the leader-follower roles is used to improve the control performance in tight environments. The efficacy of our approach is demonstrated with detailed comparisons to two alternative strategies, where it achieves the highest success rate, while completing the task fastest. 

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3. Slipstream Processors Revisited: Exploiting Branch Sets

   https://doi.org/10.1109/ISCA45697.2020.00020  

   Srinivasan, Vinesh; Chowdhury, Rangeen Basu; Rotenberg, Eric (May 2020, 2020 ACM/IEEE 47th Annual International Symposium on Computer Architecture (ISCA))

   Delinquent branches and loads remain key performance limiters in some applications. One approach to mitigate them is pre-execution. Broadly, there are two classes of pre-execution: one class repeatedly forks small helper threads, each targeting an individual dynamic instance of a delinquent branch or load; the other class begins with two redundant threads in a leader-follower arrangement, and speculatively reduces the leading thread. The objective of this paper is to design a new pre-execution microarchitecture that meets four criteria: (i) retains the simpler coordination of a leader-follower microarchitecture, (ii) is fully automated with just hardware, (iii) targets both branches and loads, (iv) and is effective. We review prior preexecution proposals and show that none of them meet all four criteria. We develop Slipstream 2.0 to meet all four criteria. The key innovation in the space of leader-follower architectures is to remove the forward control-flow slices of delinquent branches and loads, from the leading thread. This innovation overcomes key limitations in the only other hardware-only leader-follower prior works: Slipstream and Dual Core Execution (DCE). Slipstream removes backward slices of confident branches to pre-execute unconfident branches, which is ineffective in phases dominated by unconfident branches when branch pre-execution is most needed. DCE is very effective at tolerating cache-missed loads, unless their dependent branches are mispredicted. Removing forward control-flow slices of delinquent branches and delinquent loads enables two firsts, respectively: (1) leader-follower-style branch pre-execution without relying on confident instruction removal, and (2) tolerance of cache-missed loads that feed mispredicted branches. For SPEC 2006/2017 SimPoints wherein Slipstream 2.0 is auto-enabled, it achieves geomean speedups of 67%, 60%, and 12%, over baseline (one core), Slipstream, and DCE. 

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4. Guiding a Human Follower with Interaction Forces: Implications on Physical Human-Robot Interaction

   https://doi.org/10.1109/BioRob52689.2022.9925337  

   Holmes, George L.; Bonnett, Keyri Moreno; Costa, Amy; Burns, Devin; Song, Yun Seong (August 2022, 2022 9th IEEE RAS/EMBS International Conference for Biomedical Robotics and Biomechatronics (BioRob))

   This work challenges the common assumption in physical human-robot interaction (pHRI) that the movement intention of a human user can be simply modeled with dynamic equations relating forces to movements, regardless of the user. Studies in physical human-human interaction (pHHI) suggest that interaction forces carry sophisticated information that reveals motor skills and roles in the partnership and even promotes adaptation and motor learning. In this view, simple force-displacement equations often used in pHRI studies may not be sufficient. To test this, this work measured and analyzed the interaction forces (F) between two humans as the leader guided the blindfolded follower on a randomly chosen path. The actual trajectory of the follower was transformed to the velocity commands (V) that would allow a hypothetical robot follower to track the same trajectory. Then, possible analytical relationships between F and V were obtained using neural network training. Results suggest that while F helps predict V, the relationship is not straightforward, that seemingly irrelevant components of F may be important, that force-velocity relationships are unique to each human follower, and that human neural control of movement may affect the prediction of the movement intent. It is suggested that user-specific, stereotype-free controllers may more accurately decode human intent in pHRI. 

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5. An Impedance-Controlled Testbed for Simulating Variations in the Mechanical Fit of Wearable Devices

   https://doi.org/10.1109/IROS47612.2022.9981391  

   Ambrose, Alexander B.; VanAtter, Chelse; Hammond, Frank L. (October 2022, IEEE)

   The fit of a wearable device, such as a prosthesis, can be quantitatively characterized by the mechanical coupling at the user-device interface. It is thought that the mechanical impedance, specifically the stiffness and damping, of wearable device interfaces can significantly impact human performance while using them. To test this theory, we develop a forearm-mounted testbed with a motorized, two degree of freedom (2-DOF) gimbal to simulate variations in the mechanical fit of an upper-extremity wearable device during pointing and target tracking tasks. The two gimbal motors are impedance-controlled to vary the mechanical stiffness and damping between the user and the device's laser pointer end-effector. In this paper, experiments are conducted to determine the torque constants of the motors before implementation in the testbed, and to validate the accuracy of the joint impedance controller. The completed impedance-controlled wearable interface testbed is validated further by comparing the gimbal joint displacements and torques, recorded during 2-DOF base excitation experiments, to MATLAB Simulink simulation data. 

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