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Ravi Tejwani

7 accepted papers

2025

Demonstrating Shared Force-Language Embeddings for Natural Human-Robot Communication

RSS 2025poster

As robots increasingly collaborate with humans, natural language provides an intuitive interface for communication about physical actions. However, bridging the gap between linguistic descriptions and physical forces remains challenging for enabling robots to interpret movement instructions and comm…

Cited by 0PDFScholar
2024

Demonstrating Language-Grounded Motion Controller

RSS 2024poster

Recent advancements have enabled human-robot collaboration through physical assistance and verbal guidance. However, limitations persist in coordinating robots' physical motions and speech in response to real-time changes in human behavior during collaborative contact tasks. We first derive principl…

Cited by 0SourcePDFScholar
2023

An Avatar Robot Overlaid with the 3D Human Model of a Remote Operator

IROS 2023poster

Although telepresence assistive robots have made significant progress, they still lack the sense of realism and physical presence of the remote operator. This results in a lack of trust and adoption of such robots. In this paper, we introduce an Avatar Robot System which is a mixed real/virtual robo…

Cited by 5SourceScholar
2023

Zero-Shot Linear Combinations of Grounded Social Interactions with Linear Social MDPs

AAAI 2023technical

Humans and animals engage in rich social interactions. It is often theorized that a relatively small number of basic social interactions give rise to the full range of behavior observed. But no computational theory explaining how social interactions combine together has been proposed before. We do s…

Cited by 1SourcePDFScholar
2022

Incorporating Rich Social Interactions Into MDPs

ICRA 2022poster

Much of what we do as humans is engage socially with other agents, a skill that robots must also eventually possess. We demonstrate that a rich theory of social interactions originating from microsociology can be formalized by extending a nested MDP where agents reason about arbitrary functions of e…

Cited by 10SourceScholar
2019

Beyond Backprop: Online Alternating Minimization with Auxiliary Variables

ICML 2019oral

Despite significant recent advances in deep neural networks, training them remains a challenge due to the highly non-convex nature of the objective function. State-of-the-art methods rely on error backpropagation, which suffers from several well-known issues, such as vanishing and exploding gradient…