Robot Learning from Human Demonstration: Interpretation, Adaptation, and Interaction

Robot Learning from Demonstration (LfD) is a research area that focuses on how robots can learn new skills by observing how people perform various activities. As humans, we have a remarkable ability to imitate other human’s behaviors and adapt to new situations. Endowing robots with these critical capabilities is a significant but very challenging problem considering the complexity and variation of human activities in highly dynamic environments. This research focuses on how robots can learn new skills by interpreting human activities, adapting the learned skills to new situations, and naturally interacting with humans. This dissertation begins with a discussion of challenges in each of these three problems. A new unified representation approach is introduced to enable robots to simultaneously interpret the high-level semantic meanings and generalize the low-level trajectories of a broad range of human activities. An adaptive framework based on feature space decomposition is then presented for robots to not only reproduce skills, but also autonomously and efficiently adjust the learned skills to new environments that are significantly different from demonstrations. To achieve natural Human Robot Interaction (HRI), this dissertation presents a Recurrent Neural Network based deep perceptual control approach, which is capable of integrating multi-modal perception sequences with actions for robots to interact with humans in long-term tasks. Overall, by combining the above approaches, an autonomous system is created for robots to acquire important skills that can be applied to human-centered applications. Finally, this dissertation concludes with a discussion of future directions that could accelerate the upcoming technological revolution of robot learning from human demonstration

Human detection, gesture recognition, and policy generation for human-aware robots

For robots to be deployable in human occupied environments, the robots must have human-awareness and generate human-aware behaviors and policies. This thesis posits that a human-aware robot must be capable of (1) human detection and tracking, (2) human action or intent recognition and (3) intelligent, human-aware action generation. This work presents and evaluates a methodology for each stated capability. In Chapter 2, a method for practical side-by-side human detection for the Valkyrie robot using the Multisense SL sensor is presented. An explanation of why current off-the-shelf techniques are not suitable and a depth-based algorithm using point cloud descriptors and a Random Forest classifier for detecting humans under occlusion, in close proximity, in varying sparsity, and in random poses on the Multisense SL sensor are presented. In Chapter 3, action recognition of arm motion gestures is framed as a supervised learning problem. A popular technique for gesture representation with dynamic movement primitives (DMPs) and its classification using Gaussian Mixture Models (GMMs) is explored. The approach is tested under various hypotheses to understand the intricacies of using DMPs for movement representation. The following findings are reported: (a) recognition rate is sensitive to the number of basis weights, (b) DMPs can be used to recognize two linear motions, (c) rhythmic gestures can be differentiated with the discrete formulation of DMPs, and (d) DMPs can represent static-type gestures. In Chapter 4, a novel technique for (a) representing Human-Robot- Interaction as a dynamical system, and (b) using model predictive control to generate control policies is presented. The approach is motivated by using a scenario in which an Assistive Robot must be productive by bringing work to the human but must also be mindful of the human's workload. By modeling the interaction as a dynamical system, advances in control theory can be leveraged to generate useful control policies.Mechanical Engineerin

Bidirectional recurrent learning of inverse dynamic models for robots with elastic joints: a real-time real-world implementation

Collaborative robots, or cobots, are designed to work alongside humans and to alleviate their physical burdens, such as lifting heavy objects or performing tedious tasks. Ensuring the safety of human–robot interaction (HRI) is paramount for effective collaboration. To achieve this, it is essential to have a reliable dynamic model of the cobot that enables the implementation of torque control strategies. These strategies aim to achieve accurate motion while minimizing the amount of torque exerted by the robot. However, modeling the complex non-linear dynamics of cobots with elastic actuators poses a challenge for traditional analytical modeling techniques. Instead, cobot dynamic modeling needs to be learned through data-driven approaches, rather than analytical equation-driven modeling. In this study, we propose and evaluate three machine learning (ML) approaches based on bidirectional recurrent neural networks (BRNNs) for learning the inverse dynamic model of a cobot equipped with elastic actuators. We also provide our ML approaches with a representative training dataset of the cobot's joint positions, velocities, and corresponding torque values. The first ML approach uses a non-parametric configuration, while the other two implement semi-parametric configurations. All three ML approaches outperform the rigid-bodied dynamic model provided by the cobot's manufacturer in terms of torque precision while maintaining their generalization capabilities and real-time operation due to the optimized sample dataset size and network dimensions. Despite the similarity in torque estimation of these three configurations, the non-parametric configuration was specifically designed for worst-case scenarios where the robot dynamics are completely unknown. Finally, we validate the applicability of our ML approaches by integrating the worst-case non-parametric configuration as a controller within a feedforward loop. We verify the accuracy of the learned inverse dynamic model by comparing it to the actual cobot performance. Our non-parametric architecture outperforms the robot's default factory position controller in terms of accuracy.IMOCOe4.0 [EU H2020RIA-101007311]Spanish national funding [PCI2021-121925INTSENSO [MICINN-FEDER-PID2019- 109991GB-I00]INTARE (TED2021-131466B-I00) projects funded by MCIN/AEI/10.13039/501100011033EU NextGenerationEU/PRTR to ERThe SPIKEAGE [MICINN629PID2020-113422GAI00]DLROB [TED2021 131294B-I00]Spanish Ministry of Science and Innovation MCIN/AEI/10.13039/501100011033 and European Union NextGenerationEU/PRT

A survey of robot manipulation in contact

In this survey, we present the current status on robots performing manipulation tasks that require varying contact with the environment, such that the robot must either implicitly or explicitly control the contact force with the environment to complete the task. Robots can perform more and more manipulation tasks that are still done by humans, and there is a growing number of publications on the topics of (1) performing tasks that always require contact and (2) mitigating uncertainty by leveraging the environment in tasks that, under perfect information, could be performed without contact. The recent trends have seen robots perform tasks earlier left for humans, such as massage, and in the classical tasks, such as peg-in-hole, there is a more efficient generalization to other similar tasks, better error tolerance, and faster planning or learning of the tasks. Thus, in this survey we cover the current stage of robots performing such tasks, starting from surveying all the different in-contact tasks robots can perform, observing how these tasks are controlled and represented, and finally presenting the learning and planning of the skills required to complete these tasks