228 citations · 1.5k across the 123 of their papers we have counts for
129 papers
Beyond Interaction Capacity: Estimator Scaling with Recursive Models for CTR Prediction
Shivang Chopra, Fotis Iliopoulos, Zsolt Kira +1
Click-Through Rate prediction, a core task in recommendation and advertising systems, relies on modeling interactions among sparse categorical features. Explicit cross networks are…
CAPEX: Efficiently Distilling Foundation Model Behavior into Deployable Robot Policies through Experience-Adaptive Reasoning
Shivam Aarya, Zhang Xi-Jia, Chengyue Huang +6
Robot learning has largely relied on human-teleoperated demonstrations to acquire effective learnable behaviors. However, human-operated data collection processes can be unintuitiv…
Opt2VLA: Force-Aware Vision-Language-Action for Contact-Rich Humanoid Whole-Body Manipulation
Fukang Liu, Yipu Chen, Jaehwi Jang +3
Humanoid robots are expected to perform diverse human-level tasks in daily environments, many of which require precise regulation of interaction forces. While recent vision-languag…
Lies We Can See: Joint Verbal and Non-Verbal Deception by VLM Agents in Embodied Social Interactions
Jaewoo Ahn, Junseo Kim, Hyunseo Kim +4
Strategic deception by LLM and VLM agents has emerged as a central AI alignment and safety concern. Social-deduction games (where each player holds a hidden role and communicates w…
K9-Bench: Evaluating Multimodal LLMs on Canine-Centric Videos
Khush Attarde, Yusuf Ali, Megha Thukral +3
MLLMs have shown strong zero-shot capabilities across diverse inputs such as across images, video, audio, and text. A crucial, yet underexplored, application of these models lies i…
SafeManip: A Property-Driven Benchmark for Temporal Safety Evaluation in Robotic Manipulation
Chengyue Huang, Khang Vo Huynh, Sebastian Elbaum +3
Robotic manipulation is typically evaluated by task success, but reaching the correct final state does not guarantee safe execution. A robot may succeed despite contamination, prem…
Video2Sim2Real: Full-Stack Autonomous Dexterous Skill Acquisition from a Single Human Video
Yunhai Han, Jianuo Qiu, Linhao Bai +14
Human manipulation videos are a convenient and intuitive source for robot learning. However, directly transferring human dexterity to robots remains challenging due to perception e…
SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos
Jaehyeon Son, Junhyun Kim, Kyle Kam +7
Vision-language-action models (VLAs) are promising general-purpose robot policies, but adapting them to new tasks typically requires costly task-specific teleoperation data. As an…
Hide-and-Seek in Trajectories: Discovering Failure Signals for VLA Runtime Monitoring
Seongheon Park, Wendi Li, Changdae Oh +4
Vision-Language-Action (VLA) models enable robots to follow natural language instructions and generalize across diverse tasks, but they remain vulnerable to execution failures that…
The Geometry of Robustness: Optimizing Loss Landscape Curvature and Feature Manifold Alignment for Robust Finetuning of Vision-Language Models
Shivang Chopra, Shaunak Halbe, Chengyue Huang +2
Fine-tuning approaches for Vision-Language Models (VLMs) face a critical three-way trade-off between In-Distribution (ID) accuracy, Out-of-Distribution (OOD) generalization, and ad…
SAGE: Sink-Aware Grounded Decoding for Multimodal Hallucination Mitigation
Tripti Shukla, Zsolt Kira
Large vision-language models (VLMs) frequently suffer from hallucinations, generating content that is inconsistent with visual inputs. Existing methods typically address this probl…
Sim2real Image Translation Enables Viewpoint-Robust Policies from Fixed-Camera Datasets
Jeremiah Coholich, Justin Wit, Robert Azarcon +1
Vision-based policies for robot manipulation have achieved significant recent success, but are still brittle to distribution shifts such as camera viewpoint variations. Robot demon…
EVE: A Generator-Verifier System for Generative Policies
Yusuf Ali, Gryphon Patlin, Karthik Kothuri +4
Visuomotor policies based on generative such as diffusion and flow-matching have shown strong performance for robotics applications but degrade under distribution shifts, demonstra…
Toward Diffusible High-Dimensional Latent Spaces: A Frequency Perspective
Bolin Lai, Xudong Wang, Saketh Rambhatla +4
Latent diffusion has become the default paradigm for visual generation, yet we observe a persistent reconstruction-generation trade-off as latent dimensionality increases: higher-c…
Memo: Training Memory-Efficient Embodied Agents with Reinforcement Learning
Gunshi Gupta, Karmesh Yadav, Zsolt Kira +2
To enable embodied agents to operate effectively over extended timeframes, it is crucial to develop models that form and access memories to stay contextualized in their environment…
MAPS: Preserving Vision-Language Representations via Module-Wise Proximity Scheduling for Better Vision-Language-Action Generalization
Chengyue Huang, Mellon M. Zhang, Robert Azarcon +2
Vision-Language-Action (VLA) models inherit strong priors from pretrained Vision-Language Models (VLMs), but naive fine-tuning often disrupts these representations and harms genera…
EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device
Gunjan Chhablani, Xiaomeng Ye, Muhammad Zubair Irshad +1
The field of Embodied AI predominantly relies on simulation for training and evaluation, often using either fully synthetic environments that lack photorealism or high-fidelity rea…
EscherNet++: Simultaneous Amodal Completion and Scalable View Synthesis through Masked Fine-Tuning and Enhanced Feed-Forward 3D Reconstruction
Xinan Zhang, Muhammad Zubair Irshad, Anthony Yezzi +2
We propose EscherNet++, a masked fine-tuned diffusion model that can synthesize novel views of objects in a zero-shot manner with amodal completion ability. Existing approaches uti…
Let's Think in Two Steps: Mitigating Agreement Bias in MLLMs with Self-Grounded Verification
Moises Andrade, Joonhyuk Cha, Brandon Ho +3
Verifiers--functions assigning rewards to agent behavior--have been key to AI progress in math, code, and games. However, extending gains to domains without clear-cut success crite…
Hierarchical Reinforcement Learning and Value Optimization for Challenging Quadruped Locomotion
Jeremiah Coholich, Muhammad Ali Murtaza, Seth Hutchinson +1
We propose a novel hierarchical reinforcement learning framework for quadruped locomotion over challenging terrain. Our approach incorporates a two-layer hierarchy in which a high-…
FRAMES-VQA: Benchmarking Fine-Tuning Robustness across Multi-Modal Shifts in Visual Question Answering
Chengyue Huang, Brisa Maneechotesuwan, Shivang Chopra +1
Visual question answering (VQA) systems face significant challenges when adapting to real-world data shifts, especially in multi-modal contexts. While robust fine-tuning strategies…
MedMoE: Modality-Specialized Mixture of Experts for Medical Vision-Language Understanding
Shivang Chopra, Gabriela Sanchez-Rodriguez, Lingchao Mao +3
Different medical imaging modalities capture diagnostic information at varying spatial resolutions, from coarse global patterns to fine-grained localized structures. However, most…
Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models
Chengyue Huang, Yuchen Zhu, Sichen Zhu +4
Vision-language models (VLMs) are widely assumed to exhibit in-context learning (ICL), a property similar to that of their language-only counterparts. While recent work suggests VL…
FindingDory: A Benchmark to Evaluate Memory in Embodied Agents
Karmesh Yadav, Yusuf Ali, Gunshi Gupta +2
Large vision-language models have recently demonstrated impressive performance in planning and control tasks, driving interest in their application to real-world robotics. However,…