Data-driven robotic manipulation of cloth-like deformable objects : the present, challenges and future prospects

Manipulating cloth-like deformable objects (CDOs) is a long-standing problem in the robotics community. CDOs are flexible (non-rigid) objects that do not show a detectable level of compression strength while two points on the article are pushed towards each other and include objects such as ropes (1D), fabrics (2D) and bags (3D). In general, CDOs’ many degrees of freedom (DoF) introduce severe self-occlusion and complex state–action dynamics as significant obstacles to perception and manipulation systems. These challenges exacerbate existing issues of modern robotic control methods such as imitation learning (IL) and reinforcement learning (RL). This review focuses on the application details of data-driven control methods on four major task families in this domain: cloth shaping, knot tying/untying, dressing and bag manipulation. Furthermore, we identify specific inductive biases in these four domains that present challenges for more general IL and RL algorithms.Publisher PDFPeer reviewe

Contingent task and motion planning under uncertainty for human–robot interactions

Manipulation planning under incomplete information is a highly challenging task for mobile manipulators. Uncertainty can be resolved by robot perception modules or using human knowledge in the execution process. Human operators can also collaborate with robots for the execution of some difficult actions or as helpers in sharing the task knowledge. In this scope, a contingent-based task and motion planning is proposed taking into account robot uncertainty and human–robot interactions, resulting a tree-shaped set of geometrically feasible plans. Different sorts of geometric reasoning processes are embedded inside the planner to cope with task constraints like detecting occluding objects when a robot needs to grasp an object. The proposal has been evaluated with different challenging scenarios in simulation and a real environment.Postprint (published version

The development of an adaptive upper-limb stroke rehabilitation robotic system

<p>Abstract</p> <p>Background</p> <p>Stroke is the primary cause of adult disability. To support this large population in recovery, robotic technologies are being developed to assist in the delivery of rehabilitation. This paper presents an automated system for a rehabilitation robotic device that guides stroke patients through an upper-limb reaching task. The system uses a decision theoretic model (a partially observable Markov decision process, or POMDP) as its primary engine for decision making. The POMDP allows the system to automatically modify exercise parameters to account for the specific needs and abilities of different individuals, and to use these parameters to take appropriate decisions about stroke rehabilitation exercises.</p> <p>Methods</p> <p>The performance of the system was evaluated by comparing the decisions made by the system with those of a human therapist. A single patient participant was paired up with a therapist participant for the duration of the study, for a total of six sessions. Each session was an hour long and occurred three times a week for two weeks. During each session, three steps were followed: (A) after the system made a decision, the therapist either agreed or disagreed with the decision made; (B) the researcher had the device execute the decision made by the therapist; (C) the patient then performed the reaching exercise. These parts were repeated in the order of A-B-C until the end of the session. Qualitative and quantitative question were asked at the end of each session and at the completion of the study for both participants.</p> <p>Results</p> <p>Overall, the therapist agreed with the system decisions approximately 65% of the time. In general, the therapist thought the system decisions were believable and could envision this system being used in both a clinical and home setting. The patient was satisfied with the system and would use this system as his/her primary method of rehabilitation.</p> <p>Conclusions</p> <p>The data collected in this study can only be used to provide insight into the performance of the system since the sample size was limited. The next stage for this project is to test the system with a larger sample size to obtain significant results.</p

Multimodal Planning under Uncertainty: Task-Motion Planning and Collision Avoidance

openIn this thesis we investigate the problem of motion planning under environment uncertainty. Specifically, we focus on Task-Motion Planning (TMP) and probabilistic collision avoidance which are presented as two parts in this thesis. Though the two parts are largely self-contained, collision avoidance is an integral part of TMP or any robot motion planning problem in general. The problem of TMP which is the subject of Part I is by itself challenging and hence in Part I, collision computation is not the main focus and is addressed with a deterministic approach. Moreover, motion planning is performed offline since we assume static obstacles in the environment. Online TMP, incorporating dynamic obstacles or other environment changes is rather difficult due to the computational challenges associated with updating the changing task domain. As such, we devote Part II entirely to the field of online probabilistic collision avoidance motion planning. Of late, TMP for manipulation has attracted significant interest resulting in a proliferation of different approaches. In contrast, TMP for navigation has received considerably less attention. Autonomous robots operating in real-world complex scenarios require planning in the discrete (task) space and the continuous (motion) space. In knowledge-intensive domains, on the one hand, a robot has to reason at the highest-level, for example, the objects to procure, the regions to navigate to in order to acquire them; on the other hand, the feasibility of the respective navigation tasks have to be checked at the execution level. This presents a need for motion-planning-aware task planners. In Part I of this thesis, we discuss a probabilistically complete approach that leverages this task-motion interaction for navigating in large knowledge-intensive domains, returning a plan that is optimal at the task-level. The framework is intended for motion planning under motion and sensing uncertainty, which is formally known as Belief Space Planning (BSP). The underlying methodology is validated in simulation, in an office environment and its scalability is tested in the larger Willow Garage world. A reasonable comparison with a work that is closest to our approach is also provided. We also demonstrate the adaptability of our method by considering a building floor navigation domain. Finally, we also discuss the limitations of our approach and put forward suggestions for improvements and future work. In Part II of this thesis, we present a BSP framework that accounts for the landmark uncertainties during robot localization. We further extend the state-of-the-art by computing an exact expression for the collision probability under Gaussian motion and perception uncertainties. Existing BSP approaches assume that the landmark locations are well known or are known with little uncertainty. However, this might not be true in practice. Noisy sensors and imperfect motions compound to the errors originating from the estimate of environment features. Moreover, possible occlusions and dynamic objects in the environment render imperfect landmark estimation. Consequently, not considering this uncertainty can result in wrongly localizing the robot, leading to inefficient plans. Our approach incorporates the landmark uncertainty within the Bayes filter framework. We also analyze the effect of considering this uncertainty and delineate the conditions under which it can be ignored. Furthermore, we also investigate the problem of safe motion planning under Gaussian motion and sensing uncertainties. Existing approaches approximate the collision probability using upper-bounds that can lead to overly conservative estimate and thereby suboptimal plans. We formulate the collision probability process as a quadratic form in random variables. Under Gaussian distribution assumptions, an exact expression for collision probability is thus obtained which is computable in real-time. Further, we compute a tight upper bound for fast online computation of collision probability and also derive a collision avoidance constraint to be used in an optimization setting. We demonstrate and evaluate our approach using a theoretical example and simulations in single and multi-robot settings using mobile and aerial robots. A comparison of our approach to different state-of-the-art methods are also provided.openXXXIII CICLO - BIOINGEGNERIA E ROBOTICA - BIOENGINEERING AND ROBOTICSThomas, Anton