arXiv subjects

Dian Wang

Publications and source records attributed to Dian Wang.

43 records · Page 3Linked to original sources

arXiv2022

$\mathrm{SO}(2)$-Equivariant Reinforcement Learning

Equivariant neural networks enforce symmetry within the structure of their convolutional layers, resulting in a substantial improvement in sample efficiency when learning an equivariant or invariant function. Such models are applicable to robotic manipulation learning which can often be formulated as a rotationally symmetric problem. This paper studies equivariant model architectures in the context of $Q$-learning and actor-critic reinforcement learning. We identify equivariant and invariant characteristics of the optimal $Q$-function and the optimal policy and propose equivariant DQN and SAC algorithms that leverage this structure. We present experiments that demonstrate that our equivariant versions of DQN and SAC can be significantly more sample efficient than competing algorithms on an important class of robotic manipulation problems.

cs.RO↗

arXiv2022

Sample Efficient Grasp Learning Using Equivariant Models

In planar grasp detection, the goal is to learn a function from an image of a scene onto a set of feasible grasp poses in $\mathrm{SE}(2)$. In this paper, we recognize that the optimal grasp function is $\mathrm{SE}(2)$-equivariant and can be modeled using an equivariant convolutional neural network. As a result, we are able to significantly improve the sample efficiency of grasp learning, obtaining a good approximation of the grasp function after only 600 grasp attempts. This is few enough that we can learn to grasp completely on a physical robot in about 1.5 hours.

cs.RO↗

arXiv2021

Equivariant $Q$ Learning in Spatial Action Spaces

Recently, a variety of new equivariant neural network model architectures have been proposed that generalize better over rotational and reflectional symmetries than standard models. These models are relevant to robotics because many robotics problems can be expressed in a rotationally symmetric way. This paper focuses on equivariance over a visual state space and a spatial action space -- the setting where the robot action space includes a subset of $\rm{SE}(2)$. In this situation, we know a priori that rotations and translations in the state image should result in the same rotations and translations in the spatial action dimensions of the optimal policy. Therefore, we can use equivariant model architectures to make $Q$ learning more sample efficient. This paper identifies when the optimal $Q$ function is equivariant and proposes $Q$ network architectures for this setting. We show experimentally that this approach outperforms standard methods in a set of challenging manipulation problems.

cs.RO↗

arXiv2021

Action Priors for Large Action Spaces in Robotics

In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or curriculum learning. As a consequence, many researchers rely on expert demonstrations to guide learning. However, acquiring expert demonstrations can be expensive. This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks. The action prior is a probability distribution over actions that summarizes the set of policies found solving previous tasks. Our results indicate that this approach can be used to solve robotic manipulation problems that would otherwise be infeasible without expert demonstrations. Source code is available at \url{https://github.com/ondrejba/action_priors}.

cs.RO↗

arXiv2020

Policy learning in SE(3) action spaces

In the spatial action representation, the action space spans the space of target poses for robot motion commands, i.e. SE(2) or SE(3). This approach has been used to solve challenging robotic manipulation problems and shows promise. However, the method is often limited to a three dimensional action space and short horizon tasks. This paper proposes ASRSE3, a new method for handling higher dimensional spatial action spaces that transforms an original MDP with high dimensional action space into a new MDP with reduced action space and augmented state space. We also propose SDQfD, a variation of DQfD designed for large action spaces. ASRSE3 and SDQfD are evaluated in the context of a set of challenging block construction tasks. We show that both methods outperform standard baselines and can be used in practice on real robotics systems.

cs.RO↗

arXiv2019

Towards Assistive Robotic Pick and Place in Open World Environments

Assistive robot manipulators must be able to autonomously pick and place a wide range of novel objects to be truly useful. However, current assistive robots lack this capability. Additionally, assistive systems need to have an interface that is easy to learn, to use, and to understand. This paper takes a step forward in this direction. We present a robot system comprised of a robotic arm and a mobility scooter that provides both pick-and-drop and pick-and-place functionality for open world environments without modeling the objects or environment. The system uses a laser pointer to directly select an object in the world, with feedback to the user via projecting an interface into the world. Our evaluation over several experimental scenarios shows a significant improvement in both runtime and grasp success rate relative to a baseline from the literature [5], and furthermore demonstrates accurate pick and place capabilities for tabletop scenarios.

cs.RO↗

arXiv2017

Time-dependent population imaging for solid high harmonic generation

We propose an intuitive method, called time-dependent population imaging (TDPI), to map the dynamical processes of high harmonic generation (HHG) in solids by solving the time-dependent Schrödinger equation (TDSE). It is shown that the real-time dynamical characteristics of HHG in solids, such as the instantaneous photon energies of emitted harmonics, can be read directly from the energy-resolved population oscillations of electrons in the TDPIs. Meanwhile, the short and long trajectories of solid HHG are illustrated clearly from TDPI. By using the TDPI, we also investigate the effects of carrier-envelope phase (CEP) in few-cycle pulses and intuitively demonstrate the HHG dynamics driven by two-color fields. Our results show that the TDPI provides a powerful tool to study the ultrafast dynamics in strong fields for various laser-solid configurations and gain an insight into HHG processes in solids.

physics.atom-ph↗