3 citations · 5 across the 11 of their papers we have counts for
12 papers
QueST: Persistent Queries as Semantic Monitors for Drift Suppression in Long-Horizon Tracking
Mayank Anand, Mohammad Saqlain, Kyan Mahajan +3
Tracking points in videos is typically formulated as frame-to-frame correspondence, where each point is matched locally to the next frame. While this works over short horizons, err…
Object-centric Denoising Diffusion Models for Physical Reasoning
Moritz Lange, Raphael C. Engelhardt, Wolfgang Konen +2
Reasoning about the trajectories of multiple, interacting objects is integral to physical reasoning tasks in machine learning. This involves conditions imposed on the objects at di…
GRIM: Task-Oriented Grasping with Conditioning on Generative Examples
Shailesh, Alok Raj, Nayan Kumar +4
Task-Oriented Grasping (TOG) requires robots to select grasps that are functionally appropriate for a specified task - a challenge that demands an understanding of task semantics,…
SplatR : Experience Goal Visual Rearrangement with 3D Gaussian Splatting and Dense Feature Matching
Arjun P S, Andrew Melnik, Gora Chand Nandi
Experience Goal Visual Rearrangement task stands as a foundational challenge within Embodied AI, requiring an agent to construct a robust world model that accurately captures the g…
STEVE-Audio: Expanding the Goal Conditioning Modalities of Embodied Agents in Minecraft
Nicholas Lenzen, Amogh Raut, Andrew Melnik
Recently, the STEVE-1 approach has been introduced as a method for training generative agents to follow instructions in the form of latent CLIP embeddings. In this work, we present…
Object and Contact Point Tracking in Demonstrations Using 3D Gaussian Splatting
Michael Büttner, Jonathan Francis, Helge Rhodin +1
This paper introduces a method to enhance Interactive Imitation Learning (IIL) by extracting touch interaction points and tracking object movement from video demonstrations. The ap…
Towards Open-World Mobile Manipulation in Homes: Lessons from the Neurips 2023 HomeRobot Open Vocabulary Mobile Manipulation Challenge
Sriram Yenamandra, Arun Ramachandran, Mukul Khanna +42
In order to develop robots that can effectively serve as versatile and capable home assistants, it is crucial for them to reliably perceive and interact with a wide variety of obje…
Natural Language as Policies: Reasoning for Coordinate-Level Embodied Control with LLMs
Yusuke Mikami, Andrew Melnik, Jun Miura +1
We demonstrate experimental results with LLMs that address robotics task planning problems. Recently, LLMs have been applied in robotics task planning, particularly using a code ge…
Cognitive Planning for Object Goal Navigation using Generative AI Models
Arjun P S, Andrew Melnik, Gora Chand Nandi
Recent advancements in Generative AI, particularly in Large Language Models (LLMs) and Large Vision-Language Models (LVLMs), offer new possibilities for integrating cognitive plann…
Zero-shot Imitation Policy via Search in Demonstration Dataset
Federco Malato, Florian Leopold, Andrew Melnik +1
Behavioral cloning uses a dataset of demonstrations to learn a policy. To overcome computationally expensive training procedures and address the policy adaptation problem, we propo…
UniTeam: Open Vocabulary Mobile Manipulation Challenge
Andrew Melnik, Michael Büttner, Leon Harz +6
This report introduces our UniTeam agent - an improved baseline for the "HomeRobot: Open Vocabulary Mobile Manipulation" challenge. The challenge poses problems of navigation in un…
Language-Conditioned Semantic Search-Based Policy for Robotic Manipulation Tasks
Jannik Sheikh, Andrew Melnik, Gora Chand Nandi +1
Reinforcement learning and Imitation Learning approaches utilize policy learning strategies that are difficult to generalize well with just a few examples of a task. In this work,…