Humanoid robot

About: Humanoid robot is a research topic. Over the lifetime, 14387 publications have been published within this topic receiving 243674 citations. The topic is also known as: 🤖.

Collaboration in Human-Robot Teams

Abstract: Many new applications for robots require them to work alongside people as capable members of human-robot teams. These include—in the long term—robots for homes, hospitals, and offices, but already exist in more advanced settings, such as space exploration. The work reported in this paper is part of an ongoing collaboration with NASA JSC to develop Robonaut, a humanoid robot envisioned to work with human astronauts on maintenance operations for space missions. To date, work with Robonaut has mainly investigated performing a joint task with a human in which the robot is being teleoperated. However, perceptive disorientation, sensory noise, and control delays make teleoperation cognitively exhausting even for a highly skilled operator. Control delays in long range teleoperation also make shoulder-to-shoulder teamwork difficult. These issues motivate our work to make robots collaborating with people more autonomous. Our work focuses on a scenario of a human and an autonomous humanoid robot working together shoulder-to-shoulder, sharing the workspace and the objects required to complete a task. A robotic member of such a team must be able to work towards a shared goal, and be in agreement with the human as to the sequence of actions that will be required to reach that goal, as well as dynamically adjust its plan according to the human’s actions. Human-robot collaboration of this nature is an important yet relatively unexplored kind of human-robot interaction. This paper describes our work towards building a dynamic collaborative framework enabling such an interaction. We discuss our architecture and its implementation for controlling a humanoid robot, working on a task with a human partner. Our approach stems from Joint Intention Theory, which shows that for joint action to emerge, teammates must communicate to maintain a set of shared beliefs and to coordinate their actions towards the shared plan. In addition, they must demonstrate commitment to doing their own part, to the others doing theirs, to providing mutual support, and finally—to a mutual belief as to the state of the task. We argue that to this end, the concept of task and action goals is central. We therefore present a goal-driven hierarchical task representation, and a resulting collaborative turn-taking system, implementing many of the above-mentioned requirements of a robotic teammate. Additionally, we show the implementation of relevant social skills supporting our collaborative framework. Finally, we present a demonstration of our system for collaborative execution of a hierarchical object manipulation task by a robot-human team. Our humanoid robot is able to divide the task between the participants while taking into consideration the collaborator’s actions when deciding what to do next. It is capable of asking for mutual support in the cases where it is unable to perform a certain action. To facilitate this interaction, the robot actively maintains a clear and intuitive channel of communication to synchronize goals, task states, and actions, resulting in a fluid, efficient collaboration.

An anthropomorphic underactuated robotic hand with 15 dofs and a single actuator

Abstract: This paper presents the design and experimental validation of an anthropomorphic underactuated robotic hand with 15 degrees of freedom and a single actuator. First, the force transmission design of underactuated fingers is revisited. An optimal geometry of the tendon-driven fingers is then obtained. Then, underactuation between the fingers is addressed using differential mechanisms. Tendon routings are proposed and verified experimentally. Finally, a prototype of a 15-degree-of-freedom hand is built and tested. The results demonstrate the feasibility of a humanoid hand with many degrees of freedom and one single degree of actuation.

Ensemble-CIO: Full-body dynamic motion planning that transfers to physical humanoids

Abstract: While a lot of progress has recently been made in dynamic motion planning for humanoid robots, much of this work has remained limited to simulation. Here we show that executing the resulting trajectories on a Darwin-OP robot, even with local feedback derived from the optimizer, does not result in stable movements. We then develop a new trajectory optimization method, adapting our earlier CIO algorithm to plan through ensembles of perturbed models. This makes the plan robust to model uncertainty, and leads to successful execution on the robot. We obtain a high rate of task completion without trajectory divergence (falling) in dynamic forward walking, sideways walking, and turning, and a similarly high success rate in getting up from the floor (the robot broke before we could quantify the latter). Even though the planning is still done offline, the present work represents a significant step towards automating the tedious scripting of complex movements.

Modeling and Control of Legged Robots

Abstract: The promise of legged robots over wheeled robots is to provide improved mobility over rough terrain. Unfortunately, this promise comes at the cost of a significant increase in complexity. We now have a good understanding of how to make legged robots walk and run dynamically, but further research is still necessary to make them walk and run efficiently in terms of energy, speed, reactivity, versatility, and robustness. In this chapter, we will discuss how legged robots are usually modeled, how their stability analysis is approached, how dynamic motions are generated and controlled, and finally summarize the current trends in trying to improve their performance. The main problem is avoiding to fall. This can prove difficult since legged robots have to rely entirely on available contact forces to do so. The temporality of leg motions appears to be a key aspect in this respect, as current control solutions include continuous anticipation of future motion (using some form of model predictive control), or focusing more specifically on limit cycles and orbital stability.