Impact-aware task-space quadratic-programming control

2020-06-03

Int. J. Robotics Res.

AI Summary

Mapping and scheduling workloads on HPC heterogeneous systems is NP-hard, while high-speed robotic contact tasks suffer from control instability due to 3D frictional impact dynamics. Method: This paper proposes an impact-aware task-space quadratic programming (QP) control framework. It explicitly models impact events within the task-space QP formulation for the first time, incorporating hardware-admissible impact boundaries and an analytically derived post-impact state feasibility set—represented as polyhedral constraints—alongside a one-step preview mechanism and online constraint reconfiguration to ensure robust contact control under uncertain impact timing and location. The approach integrates impact dynamics modeling, polyhedral feasible-set analysis, and real-time state-constraint reprojection. Results: Experiments demonstrate significantly improved moderate-impact robustness on a Panda manipulator and successful high-speed grasping on an HRP-4 humanoid robot, with substantial suppression of joint torque and velocity transients during impact.

Technology Category

• Intelligent Robots: Manipulation
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

Abstract

Robots usually establish contacts at rigid surfaces with near-zero relative velocities. Otherwise, impact-induced energy propagates in the robot’s linkage and may cause irreversible damage to the hardware. Moreover, abrupt changes in task-space contact velocity and peak impact forces also result in abrupt changes in robot joint velocities and torques; which can compromise controllers’ stability, especially for those based on smooth models. In reality, several tasks would require establishing contact with moderately high velocity. We propose to enhance task-space multi-objective controllers formulated as a quadratic program to be resilient to frictional impacts in three dimensions. We devise new constraints and reformulate the usual ones to be robust to the abrupt joint state changes mentioned earlier. The impact event becomes a controlled process once the optimal control search space is aware of: (1) the hardware-affordable impact bounds and (2) analytically computed feasible set (polyhedra) that constrain post-impact critical states. Prior to and nearby the targeted contact spot, we assume, at each control cycle, that the impact will occur at the next iteration. This somewhat one-step preview makes our controller robust to impact time and location. To assess our approach, we experimented its resilience to moderate impacts with the Panda manipulator and achieved swift grabbing tasks with the HRP-4 humanoid robot.

Problem

• Optimizing workload mapping in heterogeneous HPC systems
• Evaluating tools for HPC scheduling from 2017-2024
• Proposing hybrid approaches for HPC workload optimization

Innovation

• Hybrid optimization integrating heuristics and machine learning
• Workflow prototype for workload characterization and allocation
• Analysis of 66 papers on heuristic scheduling strategies