Search Results for author:
Found 11 papers, 3 papers with code
Composable Part-Based Manipulation
In this paper, we propose composable part-based manipulation (CPM), a novel approach that leverages object-part decomposition and part-part correspondences to improve learning and generalization of robotic manipulation skills.
Learning Planning Abstractions from Language
This paper presents a framework for learning state and action abstractions in sequential decision-making domains.
Naturally Supervised 3D Visual Grounding with Language-Regularized Concept Learners
We propose the Language-Regularized Concept Learner (LARC), which uses constraints from language as regularization to significantly improve the accuracy of neuro-symbolic concept learners in the naturally supervised setting.
StructDiffusion: Language-Guided Creation of Physically-Valid Structures using Unseen Objects
StructDiffusion even improves the success rate of assembling physically-valid structures out of unseen objects by on average 16% over an existing multi-modal transformer model trained on specific structures.
StructFormer: Learning Spatial Structure for Language-Guided Semantic Rearrangement of Novel Objects
Geometric organization of objects into semantically meaningful arrangements pervades the built world.
Towards Robust One-shot Task Execution using Knowledge Graph Embeddings
We validated the approach on a physical platform, which resulted in the successful generalization of initial task plans to 38 of 50 execution environments with errors resulting from autonomous robot operation included.
Same Object, Different Grasps: Data and Semantic Knowledge for Task-Oriented Grasping
This is largely due to the scale of the datasets both in terms of the number of objects and tasks studied.
CAGE: Context-Aware Grasping Engine
Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks.
Robotics
Leveraging Semantics for Incremental Learning in Multi-Relational Embeddings
Service robots benefit from encoding information in semantically meaningful ways to enable more robust task execution.
Path Ranking with Attention to Type Hierarchies
The objective of the knowledge base completion problem is to infer missing information from existing facts in a knowledge base.
RoboCSE: Robot Common Sense Embedding
Autonomous service robots require computational frameworks that allow them to generalize knowledge to new situations in a manner that models uncertainty while scaling to real-world problem sizes.
