Combining Coarse and Fine Physics for Manipulation
Fast physics predictions can enable us realize real-time reactive object manipulation.
We combine coarse (i.e. fast but inaccurate) physics models, with fine (i.e. slow but accurate) physics models, to generate hybrid models that are both fast and accurate enough for real-world manipulation.
Our approach is based on parallel-in-time integration.
Using our hybrid models for planning and control, we have shown significant speedups for real-world manipulation tasks without sacrificing success rates.
Please see our ISRR 2019 paper.
Task-adaptivity in Manipulation
As humans, when we interact with objects sometimes we act fast, and other times we use slow actions in order to avoid undesired events.
How can a robot exhibit such an adaptive behaviour ?
In this work, we model the problem as a Markov decision process (MDP) with action-dependent uncertainty in the state transition function.
We consider a model where the uncertainty is proportional to the pushing speed. We provide an online solution to the MDP in real-time.
Moreover, our experiments show that a robot can exhibit an adaptive behavior during non-prehensile manipulation: pushing slow for high accuracy tasks and pushing fast for tasks that permit inaccuracy.
Please see our WAFR 2018 paper.
Online Re-planning for Manipulation in Clutter
Our goal is to grasp a target object in a cluttered environment as shown in the video.
Previous work has focused on open-loop planning followed by blind execution (without feedback). This approach can easily lead to task failures due to uncertainty. A major reason for not re-planning continuously during execution is the long planning times this domain requires.
To address this problem, we propose a stochastic trajectory optimizer and use it within a model predictive control (MPC) setting for manipulation in highly cluttered scenes.
For more details see our IEEE Humanoids 2018 paper.