Task-Based Robot Grasp Planning Using Probabilistic Inference

Robot grasp planning based on demonstrated grasp strategies

This paper presents a novel robot grasping planning approach that extracts grasp strategies (grasp type, and thumb placement and direction) from human demonstration and integrates them into the grasp planning procedure to generate a feasible grasp concerning the target object geometry and manipulation task. Our study results show that the grasp strategies of grasp type and thumb placement not o...

Scalable Multiagent Planning Using Probabilistic Inference

Multiagent planning has seen much progress with the development of formal models such as DecPOMDPs. However, the complexity of these models—NEXP-Complete even for two agents— has limited scalability. We identify certain mild conditions that are sufficient to make multiagent planning amenable to a scalable approximation w.r.t. the number of agents. This is achieved by constructing a graphical mo...

Towards Robot Task Planning From Probabilistic Models of Human Skills

We describe an algorithm for motion planning based on expert demonstrations of a skill. In order to teach robots to perform complex object manipulation tasks that can generalize robustly to new environments, we must (1) learn a representation of the effects of a task and (2) find an optimal trajectory that will reproduce these effects in a new environment. We represent robot skills in terms of ...

Task-Based Grasp Adaptation on a Humanoid Robot

In this paper, we present an approach towards autonomous grasping of objects according to their category and a given task. Recent advances in the field of object segmentation and categorization as well as task-based grasp inference have been leveraged by integrating them into one pipeline. This allows us to transfer task-specific grasp experience between objects of the same category. The effect...

Grasp Envelopes for Constraint-based Robot Motion Planning and Control