Unified Multi-Contact Fall Mitigation Planning for Humanoids via Contact Transition Tree Optimization

Wang, Shihao; Hauser, Kris

doi:10.1109/humanoids.2018.8625018

Cited by 9 publications

(4 citation statements)

References 27 publications

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“…A key element of the GS algorithms is the successor operator Γ, defined in Section IV-B. Γ allows us to attain a low branching factor and perform graph expansion more efficiently than brute-force node insertion [62]. We realize Γ for multi-contact manipulation and DcM scenarios specifically.…”

Section: A Outer Optimization Levelmentioning

confidence: 99%

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

et al. 2020

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Effective collaboration is based on online adaptation of one's own actions to the actions of their partner. This article provides a principled formalism to address online adaptation in joint planning problems such as Dyadic collaborative Manipulation (DcM) scenarios. We propose an efficient bilevel formulation that combines graph search methods with trajectory optimization, enabling robotic agents to adapt their policy on-the-fly in accordance to changes of the dyadic task. This method is the first to empower agents with the ability to plan online in hybrid spaces; optimizing over discrete contact locations, contact sequence patterns, continuous trajectories, and force profiles for co-manipulation tasks. This is particularly important in large object co-manipulation that requires changes of grasp-holds and plan adaptation. We demonstrate in simulation and with robot experiments the efficacy of the bilevel optimization by investigating the effect of robot policy changes in response to real-time alterations of the dyadic goals, eminent grasp switches, as well as optimal dyadic interactions to realize the joint task. Index Terms-Dual arm manipulation (DaM), manipulation planning, optimization and optimal control, physical human-robot interaction. I. INTRODUCTION D YADIC collaborative Manipulation (DcM) is a term we use to refer to a set of two individuals jointly manipulating an object, as shown in Fig. 1. The two individuals partner together to form a distributed system, augmenting their Manuscript

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Section: A Outer Optimization Levelmentioning

confidence: 99%

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

et al. 2020

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“…(2) collecting human kinematic, dynamic, and physiological data through motion capture systems, which are ultimately converted into the angles or moments of the corresponding joints of the robot, as shown in articles [8][9][10]. Thirdly, learning the optimal location of the multiple contact points through reinforcement learning algorithms or trajectory optimization, as introduced in articles [11][12][13][14][15]. Although these practices can reduce the harm caused by falling, they all overlook the fact that humans design robots to work in human environments, where many objects, such as walls, desks, and other fixtures, can be utilized to prevent falls.…”

Section: Introductionmentioning

confidence: 99%

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support

Zuo,

Gao,

Liu

et al. 2024

Biomimetics

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When humanoid robots work in human environments, they are prone to falling. However, when there are objects around that can be utilized, humanoid robots can leverage them to achieve balance. To address this issue, this paper established the state equation of a robot using a variable height-inverted pendulum model and implemented online trajectory optimization using model predictive control. For the arms’ optimal joint angles during movement, this paper took the distributed polygon method to calculate the arm postures. To ensure that the robot reached the target position smoothly and rapidly during its motion, this paper adopts a whole-body motion control approach, establishing a cost function for multi-objective constraints on the robot’s movement. These constraints include whole-body dynamics, center of mass constraints, arm’s end effector constraints, friction constraints, and center of pressure constraints. In the simulation, four sets of methods were compared, and the experimental results indicate that compared to free fall motion, adopting the method proposed in this paper reduces the maximum acceleration of the robot when it touches the wall to 69.1 m/s2, effectively reducing the impact force upon landing. Finally, in the actual experiment, we positioned the robot 0.85 m away from the wall and applied a forward pushing force. We observed that the robot could stably land on the wall, and the impact force was within the range acceptable to the robot, confirming the practical effectiveness of the proposed method.

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“…In particular, we consider zero-step and one-step capture motion using either foot or palm contacts. Previous approaches [9], [10] demonstrated the use of kino-dynamic optimization to compute multi-contact capture motions. However, they are either limited to special cases or computationally prohibitive to be used in a planner.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction

Lin

Righetti

Berenson

2020

IEEE Robot. Autom. Lett.

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Humanoid robots maintain balance and navigate by controlling the contact wrenches applied to the environment. While it is possible to plan dynamically-feasible motion that applies appropriate wrenches using existing methods, a humanoid may also be affected by external disturbances. Existing systems typically rely on controllers to reactively recover from disturbances. However, such controllers may fail when the robot cannot reach contacts capable of rejecting a given disturbance. In this paper, we propose a search-based footstep planner which aims to maximize the probability of the robot successfully reaching the goal without falling under disturbances. The planner considers not only the poses of the planned contact sequence, but also alternative contacts near the planned contact sequence that can be used to recover from external disturbances. Although this additional consideration significantly increases the computation load, we train neural networks to efficiently predict multi-contact zero-step and one-step capturability, which allows the planner to generate robust contact sequences efficiently. Our results show that our approach generates footstep sequences that are more robust to external disturbances than a conventional footstep planner in four challenging scenarios.

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Unified Multi-Contact Fall Mitigation Planning for Humanoids via Contact Transition Tree Optimization

Cited by 9 publications

References 27 publications

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

Online Hybrid Motion Planning for Dyadic Collaborative Manipulation via Bilevel Optimization

Whole-Body Dynamics for Humanoid Robot Fall Protection Trajectory Generation with Wall Support

Robust Humanoid Contact Planning With Learned Zero- and One-Step Capturability Prediction

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