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Found 12 papers, 2 papers with code
Multiple View Performers for Shape Completion
We propose the Multiple View Performer (MVP) - a new architecture for 3D shape completion from a series of temporally sequential views.
Multiscale Sensor Fusion and Continuous Control with Neural CDEs
Though robot learning is often formulated in terms of discrete-time Markov decision processes (MDPs), physical robots require near-continuous multiscale feedback control.
MT-Opt: Continuous Multi-Task Robotic Reinforcement Learning at Scale
In this paper, we study how a large-scale collective robotic learning system can acquire a repertoire of behaviors simultaneously, sharing exploration, experience, and representations across tasks.
Visionary: Vision architecture discovery for robot learning
We propose a vision-based architecture search algorithm for robot manipulation learning, which discovers interactions between low dimension action inputs and high dimensional visual inputs.
Disentangled Planning and Control in Vision Based Robotics via Reward Machines
In this work we augment a Deep Q-Learning agent with a Reward Machine (DQRM) to increase speed of learning vision-based policies for robot tasks, and overcome some of the limitations of DQN that prevent it from converging to good-quality policies.
Ode to an ODE
We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).
An Ode to an ODE
We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the orthogonal group O(d).
Learning Precise 3D Manipulation from Multiple Uncalibrated Cameras
In this work, we present an effective multi-view approach to closed-loop end-to-end learning of precise manipulation tasks that are 3D in nature.
MAT: Multi-Fingered Adaptive Tactile Grasping via Deep Reinforcement Learning
When this methodology is used to realize grasps from coarse initial positions provided by a vision-only planner, the system is made dramatically more robust to calibration errors in the camera-robot transform.
Workspace Aware Online Grasp Planning
This framework greatly improves the performance of standard online grasp planning algorithms by incorporating a notion of reachability into the online grasp planning process.
Robotics
Multi-Modal Geometric Learning for Grasping and Manipulation
At runtime, the network is provided a partial view of an object and tactile information is acquired to augment the captured depth information.
Robotics
Shape Completion Enabled Robotic Grasping
At runtime, a 2. 5D pointcloud captured from a single point of view is fed into the CNN, which fills in the occluded regions of the scene, allowing grasps to be planned and executed on the completed object.
Robotics
