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Found 31 papers, 10 papers with code
Sim2Real Predictivity: Does Evaluation in Simulation Predict Real-World Performance?
Second, we investigate the sim2real predictivity of Habitat-Sim for PointGoal navigation.
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.
Classification of Household Materials via Spectroscopy
To explore this, we collected a dataset of spectral measurements from two commercially available spectrometers during which a robotic platform interacted with 50 flat material objects, and we show that a neural network model can accurately analyze these measurements.
CAGE: Context-Aware Grasping Engine
Semantic grasping is the problem of selecting stable grasps that are functionally suitable for specific object manipulation tasks.
Robotics
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.
Continual Learning of Knowledge Graph Embeddings
In recent years, there has been a resurgence in methods that use distributed (neural) representations to represent and reason about semantic knowledge for robotics applications.
Semi-Supervised Haptic Material Recognition for Robots using Generative Adversarial Networks
Our approach achieves state-of-the-art results and enables a robot to estimate the material class of household objects with ~90% accuracy when 92% of the training data are unlabeled.
Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging
Finally, we present how a robot can combine this high resolution local sensing with images from the robot's head-mounted camera to achieve accurate material classification over a scene of objects on a table.
Explainable Knowledge Graph Embedding: Inference Reconciliation for Knowledge Inferences Supporting Robot Actions
Results from our algorithmic evaluation affirm our model design choices, and the results of our user studies with non-experts support the need for the proposed inference reconciliation framework.
Proactive Robot Assistance via Spatio-Temporal Object Modeling
Proactive robot assistance enables a robot to anticipate and provide for a user's needs without being explicitly asked.
Action Categorization for Computationally Improved Task Learning and Planning
This paper explores the problem of task learning and planning, contributing the Action-Category Representation (ACR) to improve computational performance of both Planning and Reinforcement Learning (RL).
Situated Structure Learning of a Bayesian Logic Network for Commonsense Reasoning
This paper details the implementation of an algorithm for automatically generating a high-level knowledge network to perform commonsense reasoning, specifically with the application of robotic task repair.
Interactive Policy Learning through Confidence-Based Autonomy
We present Confidence-Based Autonomy (CBA), an interactive algorithm for policy learning from demonstration.
Unbiasing Semantic Segmentation For Robot Perception using Synthetic Data Feature Transfer
We show that pretraining real-time segmentation architectures with synthetic segmentation data instead of ImageNet improves fine-tuning performance by reducing the bias learned in pretraining and closing the \textit{transfer gap} as a result.
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.
Active Learning within Constrained Environments through Imitation of an Expert Questioner
Active learning agents typically employ a query selection algorithm which solely considers the agent's learning objectives.
Tool Substitution with Shape and Material Reasoning Using Dual Neural Networks
This paper explores the problem of tool substitution, namely, identifying substitute tools for performing a task from a given set of candidate tools.
Leveraging Rationales to Improve Human Task Performance
Machine learning (ML) systems across many application areas are increasingly demonstrating performance that is beyond that of humans.
BIG-bench Machine Learning
Explainable Artificial Intelligence (XAI)
+1
Rearrangement: A Challenge for Embodied AI
In the rearrangement task, the goal is to bring a given physical environment into a specified state.
Explainable AI for System Failures: Generating Explanations that Improve Human Assistance in Fault Recovery
With the growing capabilities of intelligent systems, the integration of artificial intelligence (AI) and robots in everyday life is increasing.
Towards Coordinated Robot Motions: End-to-End Learning of Motion Policies on Transform Trees
The policy structure provides the user an interface to 1) specifying the spaces that are directly relevant to the completion of the tasks, and 2) designing policies for certain tasks that do not need to be learned.
Explainable AI for Robot Failures: Generating Explanations that Improve User Assistance in Fault Recovery
In order for error explanations to be meaningful, we investigate what types of information within a set of hand-scripted explanations are most helpful to non-experts for failure and solution identification.
Bi-directional Domain Adaptation for Sim2Real Transfer of Embodied Navigation Agents
Simulation offers the ability to train large numbers of robots in parallel, and offers an abundance of data.
Domain Adaptation
PointGoal Navigation
Robotics
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.
Explainable Activity Recognition for Smart Home Systems
In this work, we build on insights from Explainable Artificial Intelligence (XAI) techniques and introduce an explainable activity recognition framework in which we leverage leading XAI methods to generate natural language explanations that explain what about an activity led to the given classification.
Semantic-Based Explainable AI: Leveraging Semantic Scene Graphs and Pairwise Ranking to Explain Robot Failures
Our framework autonomously captures the semantic information in a scene to produce semantically descriptive explanations for everyday users.
Incremental Object Grounding Using Scene Graphs
This paper aims to disambiguate the human's referring expressions by allowing the agent to ask relevant questions based on semantic data obtained from scene graphs.
Subgoal-Based Explanations for Unreliable Intelligent Decision Support Systems
Intelligent decision support (IDS) systems leverage artificial intelligence techniques to generate recommendations that guide human users through the decision making phases of a task.
Creative Problem Solving in Artificially Intelligent Agents: A Survey and Framework
Creative Problem Solving (CPS) is a sub-area within Artificial Intelligence (AI) that focuses on methods for solving off-nominal, or anomalous problems in autonomous systems.
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.
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.
