Environment-aware Interactive Movement Primitives for Object Reaching in Clutter

TL;DR

This paper introduces a novel environment-aware optimization framework for 3D object reaching in cluttered spaces, improving collision avoidance and object manipulation for robotic systems.

Contribution

It presents a constrained multi-objective optimization approach (OptI-ProMP) that incorporates environment awareness and probabilistic primitives for complex 3D reaching tasks.

Findings

OptI-ProMP outperforms existing planners in collision avoidance.

Successful manipulation of pushable objects in cluttered environments.

Effective application to both low and high degree-of-freedom robots.

Abstract

The majority of motion planning strategies developed over the literature for reaching an object in clutter are applied to two dimensional (2-d) space where the state space of the environment is constrained in one direction. Fewer works have been investigated to reach a target in 3-d cluttered space, and when so, they have limited performance when applied to complex cases. In this work, we propose a constrained multi-objective optimization framework (OptI-ProMP) to approach the problem of reaching a target in a compact clutter with a case study on soft fruits grown in clusters, leveraging the local optimisation-based planner CHOMP. OptI-ProMP features costs related to both static, dynamic and pushable objects in the target neighborhood, and it relies on probabilistic primitives for problem initialisation. We tested, in a simulated poly-tunnel, both ProMP-based planners from literature and the OptI-ProMP, on low (3-dofs) and high (7-dofs) dexterity robot body, respectively. Results show collision and pushing costs minimisation with 7-dofs robot kinematics, in addition to successful static obstacles avoidance and systematic drifting from the pushable objects center of mass.