
Jeffrey Yu developed advanced motion control and manipulation features for the EPFLXplore/ERC_HD robotics platform, focusing on stability, safety, and precision in kinematics and task execution. He refined trajectory planning and servo control using C++ and Python, integrating torque sensor feedback and compliant motion to reduce drift and improve control fidelity. His work included gamepad-based sensor simulation, robust EtherCAT motor configuration, and safety measures in ROS2 launch files to prevent unintended activations. Additionally, he enhanced inverse kinematics with fingertip-aware corrections and expanded MoveIt planning to support complex object manipulation and sand-task automation, demonstrating depth in robotics and embedded systems engineering.

April 2025 — EPFLXplore/ERC_HD: Delivered three integrated features focusing on precision IK, perception/manipulation, and sand-task automation. No major bugs fixed this month; path-constraint issues are being tracked for future fixes. Overall impact: enhanced manipulation accuracy, richer planning with lidar/clam objects, and robust sand-handling workflows that enable more reliable demonstrations and potential field deployments. Key technologies demonstrated include transform-based finger-type corrections, fingertip rotations in IK, MoveIt object integration (lidar/clam), trajectory planning refinements for sand objects, and tool pickup/drop pipelines.
April 2025 — EPFLXplore/ERC_HD: Delivered three integrated features focusing on precision IK, perception/manipulation, and sand-task automation. No major bugs fixed this month; path-constraint issues are being tracked for future fixes. Overall impact: enhanced manipulation accuracy, richer planning with lidar/clam objects, and robust sand-handling workflows that enable more reliable demonstrations and potential field deployments. Key technologies demonstrated include transform-based finger-type corrections, fingertip rotations in IK, MoveIt object integration (lidar/clam), trajectory planning refinements for sand objects, and tool pickup/drop pipelines.
During December 2024, EPFLXplore/ERC_HD delivered substantial stability, safety, and testability gains across motion control and kinematics. Key progress includes: refined trajectory planner usage and servo planning with directional torque sensor; robust torque sensor input binding; enabled compliant motion; updated EtherCAT motor control configurations; introduced gamepad-based torque sensor for testing while preserving legacy IK logic; and safety hardening by disabling motor nodes in the launch file to prevent unintended launches. These changes reduce drift, improve control fidelity, facilitate safer testing, and establish a foundation for future IK enhancements.
During December 2024, EPFLXplore/ERC_HD delivered substantial stability, safety, and testability gains across motion control and kinematics. Key progress includes: refined trajectory planner usage and servo planning with directional torque sensor; robust torque sensor input binding; enabled compliant motion; updated EtherCAT motor control configurations; introduced gamepad-based torque sensor for testing while preserving legacy IK logic; and safety hardening by disabling motor nodes in the launch file to prevent unintended launches. These changes reduce drift, improve control fidelity, facilitate safer testing, and establish a foundation for future IK enhancements.
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