Real-time Motion Planning for Robotic Teleoperation Using Dynamic-goal Deep Reinforcement Learning
Bibliographic record
Abstract
We propose Dynamic-goal Deep Reinforcement Learning (DGDRL) method to address the problem of robot arm motion planning in telemanipulation applications. This method intuitively maps human hand motions to a robot arm in real-time, while avoiding collisions, joint limits and singularities. We further propose a novel hardware setup, based on the HTC VIVE VR system, that enables users to smoothly control the robot tool position and orientation with hand motions, while monitoring its movements in a 3D virtual reality environment. A VIVE controller captures 6D hand movements and gives them as reference trajectories to a deep neural policy network for controlling the robot’s joint movements. Our DGDRL method leverages the state-of-art Proximal Policy Optimization (PPO) algorithm for deep reinforcement learning to train the policy network with the robot joint values and reference trajectory observed at each iteration. Since training the network on a real robot is time-consuming and unsafe, we developed a simulation environment called RobotPath which provides kinematic modeling, collision analysis and a 3D VR graphical simulation of industrial robots. The deep neural network trained using RobotPath is then deployed on a physical robot (ABB IRB 120) to evaluate its performance. We show that the policies trained in the simulation environment can be successfully used for trajectory planning on a real robot. The the codes, data and video presenting our experiments are available at https://github.com/kavehkamali/ppoRobotPath.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".