Exploration of geometry and forces occurring within human-to-robot handovers

Pan, Matthew K. X. J.; Croft, Elizabeth A.; Niemeyer, G.

doi:10.1109/haptics.2018.8357196

Cited by 18 publications

(16 citation statements)

References 22 publications

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“…There is no agreement on what factors are most important in determining how handovers are carried out between two humans, let alone between a robot and a human. Although most research has focused on a robot handing objects to humans, there have also been studies on robots taking objects from humans (93)(94)(95). Additionally, several efforts have been aimed at enabling a robot to manipulate objects jointly with humans, and the act of carrying objects jointly with humans has been demonstrated using both humanoid robots (96)(97)(98)(99) and mobile manipulators (100)(101)(102)(103).…”

Section: Learning For Manipulationmentioning

confidence: 99%

Trends and challenges in robot manipulation

Billard

Kragić

2019

Science

527

236

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Dexterous manipulation is one of the primary goals in robotics. Robots with this capability could sort and package objects, chop vegetables, and fold clothes. As robots come to work side by side with humans, they must also become human-aware. Over the past decade, research has made strides toward these goals. Progress has come from advances in visual and haptic perception and in mechanics in the form of soft actuators that offer a natural compliance. Most notably, immense progress in machine learning has been leveraged to encapsulate models of uncertainty and to support improvements in adaptive and robust control. Open questions remain in terms of how to enable robots to deal with the most unpredictable agent of all, the human.

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Section: Learning For Manipulationmentioning

confidence: 99%

Trends and challenges in robot manipulation

Billard

Kragić

2019

Science

527

236

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“…The simplest online controllers for the reach phase of handovers take a visual servoing approach, i.e., driving the robot towards the human hand [8]- [10]. This controller updates the robot's motion plan continuously by generating velocities proportional to the error between the human hand's position and the robot gripper's position.…”

Section: A Human-robot Handover Reach Phase Controllersmentioning

confidence: 99%

Evaluating Guided Policy Search for Human-Robot Handovers

Kshirsagar

Hoffman

Biess

2021

IEEE Robot. Autom. Lett.

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We evaluate the potential of Guided Policy Search (GPS), a model-based reinforcement learning (RL) method, to train a robot controller for human-robot object handovers. Handovers are a key competency for collaborative robots and GPS could be a promising approach for this task, as it is data efficient and does not require prior knowledge of the robot and environment dynamics. However, existing uses of GPS did not consider important aspects of human-robot handovers, namely large spatial variations in reach locations, moving targets, and generalizing over mass changes induced by the object being handed over. In this work, we formulate the reach phase of handovers as an RL problem and then train a collaborative robot arm in a simulation environment. Our results indicate that GPS is limited in the spatial generalizability over variations in the target location, but that this issue can be mitigated with the addition of local controllers trained over target locations in the high error regions. Moreover, learned policies generalize well over a large range of end-effector masses. Moving targets can be reached with comparable errors using a global policy trained on static targets, but this results in inefficient, high-torque, trajectories. Training on moving targets improves trajectories, but results in worse worst-case performance. Initial results suggest that lower-dimensional state representations are beneficial for GPS performance in handovers.

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“…We adopt a texture-based approach that tracks 3D key points on the object surface detected by an eye-in-hand RGB-D camera. Hence, the robot directly tracks the object rather than the human hand, and no additional markers are needed, differently from the marker-based solutions proposed by Medina et al (2016) and by Pan et al (2018). Among the marker-less approaches, recent works are (Nemlekar et al, 2019;Yang et al, 2020;Rosenberger et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Handover Control for Human-Robot and Robot-Robot Collaboration

2021

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Modern scenarios in robotics involve human-robot collaboration or robot-robot cooperation in unstructured environments. In human-robot collaboration, the objective is to relieve humans from repetitive and wearing tasks. This is the case of a retail store, where the robot could help a clerk to refill a shelf or an elderly customer to pick an item from an uncomfortable location. In robot-robot cooperation, automated logistics scenarios, such as warehouses, distribution centers and supermarkets, often require repetitive and sequential pick and place tasks that can be executed more efficiently by exchanging objects between robots, provided that they are endowed with object handover ability. Use of a robot for passing objects is justified only if the handover operation is sufficiently intuitive for the involved humans, fluid and natural, with a speed comparable to that typical of a human-human object exchange. The approach proposed in this paper strongly relies on visual and haptic perception combined with suitable algorithms for controlling both robot motion, to allow the robot to adapt to human behavior, and grip force, to ensure a safe handover. The control strategy combines model-based reactive control methods with an event-driven state machine encoding a human-inspired behavior during a handover task, which involves both linear and torsional loads, without requiring explicit learning from human demonstration. Experiments in a supermarket-like environment with humans and robots communicating only through haptic cues demonstrate the relevance of force/tactile feedback in accomplishing handover operations in a collaborative task.

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Exploration of geometry and forces occurring within human-to-robot handovers

Cited by 18 publications

References 22 publications

Trends and challenges in robot manipulation

Trends and challenges in robot manipulation

Evaluating Guided Policy Search for Human-Robot Handovers

Handover Control for Human-Robot and Robot-Robot Collaboration

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