Figure 1 - uploaded by Kai Ploeger
Content may be subject to copyright.
The juggling movement consisting of four separate movements, which are repeated to achieve juggling of two balls with a single anthropomorphic manipulator: (a) throw ball 1, (b) catch ball 2, (c) throw ball 2, (d) catch ball one, (e) repeat.
Source publication
Robots that can learn in the physical world will be important to en-able robots to escape their stiff and pre-programmed movements. For dynamic high-acceleration tasks, such as juggling, learning in the real-world is particularly challenging as one must push the limits of the robot and its actuation without harming the system, amplifying the necess...
Contexts in source publication
Context 1
... desired juggling movement for two balls consists of four repeated movements, i.e., (a) throwing the first ball, (b) catching the second ball (c) throwing the second ball, and (d) catching the first ball (Fig. 1). We define the switching points between these movements as the via-points of the policy and to achieve a limit-cycle, we keep repeating these via-points. The cyclic pattern is prepended with an initial stroke movement that quickly enters the limit cycle without dropping a ball. Applying the limit cycles PD-references from the start ...
Context 2
... desired juggling movement for two balls consists of four repeated movements, i.e., (a) throwing the first ball, (b) catching the second ball (c) throwing the second ball, and (d) catching the first ball ( Fig. 1). We define the switching points between these movements as the via-points of the policy and to achieve a limit-cycle, we keep repeating these via-points. The cyclic pattern is prepended with an initial stroke movement that quickly enters the limit cycle without dropping a ball. Applying the limit cycles PD-references from the start ...
Similar publications
- [...]
For the dual peg-in-hole compliance assembly task of upper and lower double-hole structural micro-devices, a skill-learning method is proposed. This method combines offline training in a simulation space and online training in a realistic space. In this paper, a dual peg-in-hole model is built according to the results of a force analysis, and conta...
In this paper, a Q-learning-based task scheduling approach for mixed-critical application on heterogeneous multi-cores (QSMix) to optimize their main design challenges is proposed. This approach employs reinforcement learning capabilities to optimize execution time, power consumption, reliability and temperature of the heterogeneous multi-cores dur...
Reinforcement learning refers to powerful algorithms for solving goal related problems by maximizing the reward over many time steps. By incorporating them into the dynamic movement primitives (DMPs) which are now widely used parametric representations in robotics, movements obtained from a single human demonstration can be adapted so that a robot...
We develop theory and algorithms for average-reward on-policy Reinforcement Learning (RL). We first consider bounding the difference of the long-term average reward for two policies. We show that previous work based on the discounted return (Schulman et al., 2015; Achiam et al., 2017) results in a non-meaningful bound in the average-reward setting....
For effective interactions with the open world, robots should understand how interactions with known and novel objects help them towards their goal. A key aspect of this understanding lies in detecting an object's affordances, which represent the potential effects that can be achieved by manipulating the object in various ways. Our approach leverag...
