Papers by Tanmana Sadhu
Open-World Planning via Lifted Regression with LLM-Inferred Affordances for Embodied Agents (2025.acl-long)
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Xiaotian Liu, Ali Pesaranghader, Hanze Li, Punyaphat Sukcharoenchaikul, Jaehong Kim, Tanmana Sadhu, Hyejeong Jeon, Scott Sanner
| Challenge: | Existing open-world planning methods rely on closed-world assumption (CWA) symbolic planners face combinatorial explosion of states and actions due to reliance on grounding. |
| Approach: | They propose an open-world planning approach integrating lifted regression with LLM-generated affordances. |
| Outcome: | The proposed approach outperforms state-of-the-art LLM planners and a grounded planner on three benchmarks. |
Athena: Safe Autonomous Agents with Verbal Contrastive Learning (2024.emnlp-industry)
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| Challenge: | Existing safety benchmarks on the ability of large language models to perform tasks are lacking. |
| Approach: | They propose a framework that leverages verbal contrastive learning to guide agents towards safety . they use past safe and unsafe trajectories as in-context examples to guide them towards safety. |
| Outcome: | The proposed framework leverages verbal contrastive learning to guide agents towards safety while performing tasks. |
VestaBench: An Embodied Benchmark for Safe Long-Horizon Planning Under Multi-Constraint and Adversarial Settings (2025.emnlp-industry)
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| Challenge: | Existing safety benchmarks do not represent a diverse range of multi-constraint tasks that require long-horizon planning with a focus on safety. |
| Approach: | They propose a benchmark to assess the safety of embodied AI agents under multiple constraints. |
| Outcome: | The proposed benchmarks show that LLMs perform poorly against their tasks . they also suffer significantly compromised safety outcomes . |
Exploring Compositional Image Retrieval with Hybrid Compositional Learning and Heuristic Negative Mining (2022.findings-emnlp)
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| Challenge: | Existing CIR models are pre-trained on uni-modal data, resulting in unimodal data. |
| Approach: | They propose a CIR model HyCoLe-HNM with CLIP as the backbone . they use a gated fusion mechanism from a question answering model to perform compositional learning . |
| Outcome: | The proposed model achieves state-of-the-art performance on three CIR datasets . it borrows a gated fusion mechanism from a question answering model to perform compositional fusion . |