| 1. |
- D'Accolti, D., et al.
(författare)
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Online Classification of Transient EMG Patterns for the Control of the Wrist and Hand in a Transradial Prosthesis
- 2023
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766 .- 2377-3774. ; 8:2, s. 1045-1052
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Tidskriftsartikel (refereegranskat)abstract
- Decoding human motor intentions by processing electrophysiological signals is a crucial, yet unsolved, challenge for the development of effective upper limb prostheses. Pattern recognition of continuous myoelectric (EMG) signals represents the state-of-art for multi-DoF prosthesis control. However, this approach relies on the unreliable assumption that repeatable muscular contractions produce repeatable patterns of steady-state EMGs. Here, we propose an approach for decoding wrist and hand movements by processing the signals associated with the onset of contraction (transient EMG). Specifically, we extend the concept of a transient EMG controller for the control of both wrist and hand, and tested it online. We assessed it with one transradial amputee and 15 non-amputees via the Target Achievement Control test. Non-amputees successfully completed 95% of the trials with a median completion time of 17 seconds, showing a significant learning trend (p < 0.001). The transradial amputee completed about the 80% of the trials with a median completion time of 26 seconds. Although the performance proved comparable with earlier studies, the long completion times suggest that the current controller is not yet clinically viable. However, taken collectively, our outcomes reinforce earlier hypothesis that the transient EMG could represent a viable alternative to steady-state pattern recognition approaches.
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| 2. |
- Marta, Daniel, et al.
(författare)
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Human-Feedback Shield Synthesis for Perceived Safety in Deep Reinforcement Learning
- 2022
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766 .- 2377-3774. ; 7:1, s. 406-413
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Despite the successes of deep reinforcement learning (RL), it is still challenging to obtain safe policies. Formal verifi- cation approaches ensure safety at all times, but usually overly restrict the agent’s behaviors, since they assume adversarial behavior of the environment. Instead of assuming adversarial behavior, we suggest to focus on perceived safety instead, i.e., policies that avoid undesired behaviors while having a desired level of conservativeness. To obtain policies that are perceived as safe, we propose a shield synthesis framework with two distinct loops: (1) an inner loop that trains policies with a set of actions that is constrained by shields whose conservativeness is parameterized, and (2) an outer loop that presents example rollouts of the policy to humans and collects their feedback to update the parameters of the shields in the inner loop. We demonstrate our approach on a RL benchmark of Lunar landing and a scenario in which a mobile robot navigates around humans. For the latter, we conducted two user studies to obtain policies that were perceived as safe. Our results indicate that our framework converges to policies that are perceived as safe, is robust against noisy feedback, and can query feedback for multiple policies at the same time.
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| 3. |
- Rodriguez de Cos, Carlos, 1992-, et al.
(författare)
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Adaptive Cooperative Control for Human-Robot Load Manipulation
- 2022
-
Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766 .- 2377-3774. ; , s. 5623-5630
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Tidskriftsartikel (refereegranskat)abstract
- In this letter, we propose a control strategy forhuman-robot cooperative manipulation under the ambiguous collaboration of a human agent. To cope with this uncertainty, an adaptive update law inferring the human contribution to the system dynamics from basic perception feedback through the human arm stiffness is used. Furthermore, the robustness and accuracy of the approach is enhanced by redundantly tracking the shared load references and its associated end-effector position references. To validate the control strategy, both theoretical Lyapunov stability analysis and experimental results –employing two robot manipulators with 6 degrees of freedom under external disturbances– are provided.
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| 4. |
- Tan, Kaige, et al.
(författare)
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Shape Estimation of a 3D Printed Soft Sensor Using Multi-Hypothesis Extended Kalman Filter
- 2022
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766 .- 2377-3774. ; 7:3, s. 8383-8390
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Tidskriftsartikel (refereegranskat)abstract
- This study develops a multi-hypothesis extended Kalman filter (MH-EKF) for the online estimation of the bending angle of a 3D printed soft sensor attached to soft actuators. Despite the advantage of compliance and low interference, the 3D printed soft sensor is susceptible to the hysteresis property and nonlinear effects. Improving measurement accuracy for sensors with hysteresis is a common challenge. Current studies mainly apply complex models and highly nonlinear functions to characterize the hysteresis, requiring a complicated parameter identification process and challenging real-time applications. This study enhances the model simplicity and the real-time performance for the hysteresis characterization. We identify the hysteresis by combining multiple polynomial functions and improving the sensor estimation with the proposed MH-EKF. We examine the performance of the filter in the real-time closed-loop control system. Compared with the baseline methods, the proposed approach shows improvements in the estimation accuracy with low computational complexity.
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| 5. |
- Wiberg, Viktor, et al.
(författare)
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Control of rough terrain vehicles using deep reinforcement learning
- 2022
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766 .- 2377-3774. ; 7:1, s. 390-397
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Tidskriftsartikel (refereegranskat)abstract
- We explore the potential to control terrain vehicles using deep reinforcement in scenarios where human operators and traditional control methods are inadequate. This letter presents a controller that perceives, plans, and successfully controls a 16-tonne forestry vehicle with two frame articulation joints, six wheels, and their actively articulated suspensions to traverse rough terrain. The carefully shaped reward signal promotes safe, environmental, and efficient driving, which leads to the emergence of unprecedented driving skills. We test learned skills in a virtual environment, including terrains reconstructed from high-density laser scans of forest sites. The controller displays the ability to handle obstructing obstacles, slopes up to 27 degrees, and a variety of natural terrains, all with limited wheel slip, smooth, and upright traversal with intelligent use of the active suspensions. The results confirm that deep reinforcement learning has the potential to enhance control of vehicles with complex dynamics and high-dimensional observation data compared to human operators or traditional control methods, especially in rough terrain.
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| 6. |
- Abdul Khader, Shahbaz, et al.
(författare)
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Data-Efficient Model Learning and Prediction for Contact-Rich Manipulation Tasks
- 2020
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 5:3, s. 4321-4328
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Tidskriftsartikel (refereegranskat)abstract
- In this letter, we investigate learning forward dynamics models and multi-step prediction of state variables (long-term prediction) for contact-rich manipulation. The problems are formulated in the context of model-based reinforcement learning (MBRL). We focus on two aspects-discontinuous dynamics and data-efficiency-both of which are important in the identified scope and pose significant challenges to State-of-the-Art methods. We contribute to closing this gap by proposing a method that explicitly adopts a specific hybrid structure for the model while leveraging the uncertainty representation and data-efficiency of Gaussian process. Our experiments on an illustrative moving block task and a 7-DOF robot demonstrate a clear advantage when compared to popular baselines in low data regimes.
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| 7. |
- Abdul Khader, Shahbaz, et al.
(författare)
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Learning deep energy shaping policies for stability-guaranteed manipulation
- 2021
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Ingår i: IEEE Robotics and Automation Letters. - : Institute of Electrical and Electronics Engineers (IEEE). - 2377-3766. ; 6:4, s. 8583-8590
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Tidskriftsartikel (refereegranskat)abstract
- Deep reinforcement learning (DRL) has been successfully used to solve various robotic manipulation tasks. However, most of the existing works do not address the issue of control stability. This is in sharp contrast to the control theory community where the well-established norm is to prove stability whenever a control law is synthesized. What makes traditional stability analysis difficult for DRL are the uninterpretable nature of the neural network policies and unknown system dynamics. In this work, stability is obtained by deriving an interpretable deep policy structure based on the energy shaping control of Lagrangian systems. Then, stability during physical interaction with an unknown environment is established based on passivity. The result is a stability guaranteeing DRL in a model-free framework that is general enough for contact-rich manipulation tasks. With an experiment on a peg-in-hole task, we demonstrate, to the best of our knowledge, the first DRL with stability guarantee on a real robotic manipulator.
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| 8. |
- Adolfsson, Daniel, 1992-, et al.
(författare)
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TBV Radar SLAM - Trust but Verify Loop Candidates
- 2023
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Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 8:6, s. 3613-3620
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Tidskriftsartikel (refereegranskat)abstract
- Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a robust odometry pipeline within a pose graph framework. By evaluation on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it generalizes across environments without needing to change any parameters. We provide the open-source implementation at https://github.com/dan11003/tbv_slam_public
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| 9. |
- Ak, Abdullah Cihan, et al.
(författare)
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Learning Failure Prevention Skills for Safe Robot Manipulation
- 2023
-
Ingår i: IEEE Robotics and Automation Letters. - Piscataway, NJ : IEEE. - 2377-3766. ; 8:12, s. 7994-8001
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Tidskriftsartikel (refereegranskat)abstract
- Robots are more capable of achieving manipulation tasks for everyday activities than before. However, the safety of manipulation skills that robots employ is still an open problem. Considering all possible failures during skill learning increases the complexity of the process and restrains learning an optimal policy. Nonetheless, safety-focused modularity in the acquisition of skills has not been adequately addressed in previous works. For that purpose, we reformulate skills as base and failure prevention skills, where base skills aim at completing tasks and failure prevention skills aim at reducing the risk of failures to occur. Then, we propose a modular and hierarchical method for safe robot manipulation by augmenting base skills by learning failure prevention skills with reinforcement learning and forming a skill library to address different safety risks. Furthermore, a skill selection policy that considers estimated risks is used for the robot to select the best control policy for safe manipulation. Our experiments show that the proposed method achieves the given goal while ensuring safety by preventing failures. We also show that with the proposed method, skill learning is feasible and our safe manipulation tools can be transferred to the real environment © 2023 IEEE
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| 10. |
- Almeida, Tiago Rodrigues de, 1996-, et al.
(författare)
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Trajectory Prediction for Heterogeneous Agents : A Performance Analysis on Small and Imbalanced Datasets
- 2024
-
Ingår i: IEEE Robotics and Automation Letters. - : IEEE. - 2377-3766. ; 9:7, s. 6576-6583
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Tidskriftsartikel (refereegranskat)abstract
- Robots and other intelligent systems navigating in complex dynamic environments should predict future actions and intentions of surrounding agents to reach their goals efficiently and avoid collisions. The dynamics of those agents strongly depends on their tasks, roles, or observable labels. Class-conditioned motion prediction is thus an appealing way to reduce forecast uncertainty and get more accurate predictions for heterogeneous agents. However, this is hardly explored in the prior art, especially for mobile robots and in limited data applications. In this paper, we analyse different class-conditioned trajectory prediction methods on two datasets. We propose a set of conditional pattern-based and efficient deep learning-based baselines, and evaluate their performance on robotics and outdoors datasets (TH & Ouml;R-MAGNI and Stanford Drone Dataset). Our experiments show that all methods improve accuracy in most of the settings when considering class labels. More importantly, we observe that there are significant differences when learning from imbalanced datasets, or in new environments where sufficient data is not available. In particular, we find that deep learning methods perform better on balanced datasets, but in applications with limited data, e.g., cold start of a robot in a new environment, or imbalanced classes, pattern-based methods may be preferable.
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