Search Results for author:
Found 10 papers, 6 papers with code
InstructPipe: Building Visual Programming Pipelines with Human Instructions
Our user study (N=16) showed that InstructPipe empowers novice users to streamline their workflow in creating desired ML pipelines, reduce their learning curve, and spark innovative ideas with open-ended commands.
Learning to Fold Real Garments with One Arm: A Case Study in Cloud-Based Robotics Research
Autonomous fabric manipulation is a longstanding challenge in robotics, but evaluating progress is difficult due to the cost and diversity of robot hardware.
Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language
Large pretrained (e. g., "foundation") models exhibit distinct capabilities depending on the domain of data they are trained on.
Ranked #21 on
Video Retrieval
on MSR-VTT-1kA
(video-to-text R@1 metric)
Implicit Behavioral Cloning
We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models.
Spatial Action Maps for Mobile Manipulation
Typical end-to-end formulations for learning robotic navigation involve predicting a small set of steering command actions (e. g., step forward, turn left, turn right, etc.)
Grasping in the Wild:Learning 6DoF Closed-Loop Grasping from Low-Cost Demonstrations
A key aspect of our grasping model is that it uses "action-view" based rendering to simulate future states with respect to different possible actions.
Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly
This formulation enables the model to acquire a broader understanding of how shapes and surfaces fit together for assembly -- allowing it to generalize to new objects and kits.
ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
To address these challenges, we present ClearGrasp -- a deep learning approach for estimating accurate 3D geometry of transparent objects from a single RGB-D image for robotic manipulation.
Ranked #1 on
Semantic Segmentation
on Cleargrasp (Novel)
TossingBot: Learning to Throw Arbitrary Objects with Residual Physics
In this work, we propose an end-to-end formulation that jointly learns to infer control parameters for grasping and throwing motion primitives from visual observations (images of arbitrary objects in a bin) through trial and error.
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement Learning
Skilled robotic manipulation benefits from complex synergies between non-prehensile (e. g. pushing) and prehensile (e. g. grasping) actions: pushing can help rearrange cluttered objects to make space for arms and fingers; likewise, grasping can help displace objects to make pushing movements more precise and collision-free.
