Author name cluster

Enna Sachdeva

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6 papers

2 author rows

ICRA Conference 2025 Conference Paper

Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-Constructed Hierarchical Trees

Piyush Gupta
David Isele
Enna Sachdeva
Pin-Hao Huang
Behzad Dariush
Kwonjoon Lee
Sangjae Bae

We present a novel mission-planning strategy for heterogeneous multi-robot teams, taking into account the specific constraints and capabilities of each robot. Our approach employs hierarchical trees to systematically break down complex missions into manageable sub-tasks. We develop specialized APIs and tools, which are utilized by Large Language Models (LLMs) to efficiently construct these hierarchical trees. Once the hierarchical tree is generated, it is further decomposed to create optimized schedules for each robot, ensuring adherence to their individual constraints and capabilities. We demonstrate the effectiveness of our framework through detailed examples covering a wide range of missions, showcasing its flexibility and scalability.

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NeurIPS Conference 2024 Conference Paper

Estimating Ego-Body Pose from Doubly Sparse Egocentric Video Data

Seunggeun Chi
Pin-Hao Huang
Enna Sachdeva
Hengbo Ma
Karthik Ramani
Kwonjoon Lee

We study the problem of estimating the body movements of a camera wearer from egocentric videos. Current methods for ego-body pose estimation rely on temporally dense sensor data, such as IMU measurements from spatially sparse body parts like the head and hands. However, we propose that even temporally sparse observations, such as hand poses captured intermittently from egocentric videos during natural or periodic hand movements, can effectively constrain overall body motion. Naively applying diffusion models to generate full-body pose from head pose and sparse hand pose leads to suboptimal results. To overcome this, we develop a two-stage approach that decomposes the problem into temporal completion and spatial completion. First, our method employs masked autoencoders to impute hand trajectories by leveraging the spatiotemporal correlations between the head pose sequence and intermittent hand poses, providing uncertainty estimates. Subsequently, we employ conditional diffusion models to generate plausible full-body motions based on these temporally dense trajectories of the head and hands, guided by the uncertainty estimates from the imputation. The effectiveness of our methods was rigorously tested and validated through comprehensive experiments conducted on various HMD setup with AMASS and Ego-Exo4D datasets. Project page: https: //sgchi. github. io/dsposer

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ICRA Conference 2024 Conference Paper

Optimal Driver Warning Generation in Dynamic Driving Environment

Chenran Li
Aolin Xu 0002
Enna Sachdeva
Teruhisa Misu
Behzad Dariush

The driver warning system that alerts the human driver about potential risks during driving is a key feature of an advanced driver assistance system. Existing driver warning technologies, mainly the forward collision warning and unsafe lane change warning, can reduce the risk of collision caused by human errors. However, the current design methods have several major limitations. Firstly, the warnings are mainly generated in a one-shot manner without modeling the ego driver’s reactions and surrounding objects, which reduces the flexibility and generality of the system over different scenarios. Additionally, the triggering conditions of warning are mostly rule-based threshold-checking given the current state, which lacks the prediction of the potential risk in a sufficiently long future horizon. In this work, we study the problem of optimally generating driver warnings by considering the interactions among the generated warning, the driver behavior, and the states of ego and surrounding vehicles on a long horizon. The warning generation problem is formulated as a partially observed Markov decision process (POMDP). An optimal warning generation framework is proposed as a solution to the proposed POMDP. The simulation experiments demonstrate the superiority of the proposed solution to the existing warning generation methods.

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IROS Conference 2022 Conference Paper

Domain Knowledge Driven Pseudo Labels for Interpretable Goal-Conditioned Interactive Trajectory Prediction

Lingfeng Sun
Chen Tang 0001
Yaru Niu
Enna Sachdeva
Chiho Choi
Teruhisa Misu
Masayoshi Tomizuka
Wei Zhan

Motion forecasting in highly interactive scenarios is a challenging problem in autonomous driving. In such scenarios, we need to accurately predict the joint behavior of interacting agents to ensure the safe and efficient navigation of autonomous vehicles. Recently, goal-conditioned methods have gained increasing attention due to their advantage in performance and their ability to capture the multimodality in trajec-tory distribution. In this work, we study the joint trajectory prediction problem with the goal-conditioned framework. In particular, we introduce a conditional-variational-autoencoder-based (CVAE) model to explicitly encode different interaction modes into the latent space. However, we discover that the vanilla model suffers from posterior collapse and cannot induce an informative latent space as desired. To address these issues, we propose a novel approach to avoid KL vanishing and induce an interpretable interactive latent space with pseudo labels. The proposed pseudo labels allow us to incorporate domain knowledge on interaction in a flexible manner. We motivate the proposed method using an illustrative toy example. In addition, we validate our framework on the Waymo Open Motion Dataset with both quantitative and qualitative evaluations.

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AAMAS Conference 2021 Conference Paper

Dynamic Skill Selection for Learning Joint Actions

Enna Sachdeva
Shauharda Khadka
Somdeb Majumdar
Kagan Tumer

Learning in tightly coupled multiagent settings with sparse rewards is challenging because multiple agents must reach the goal state simultaneously for the team to receive a reward. This is even more challenging under temporal coupling constraints - where agents need to sequentially complete different components of a task in a particular order. Here, a single local reward is inadequate for learning an effective policy. We introduce MADyS, Multiagent Learning via Dynamic Skill Selection, a bi-level optimization framework that learns to dynamically switch between multiple local skills to optimize sparse team objectives. MADyS adopts fast policy gradients to learn local skills using local rewards and an evolutionary algorithm to optimize the sparse team objective by recruiting the most optimal skill at any given time. This eliminates the need to generate a single dense reward via reward shaping or other mixing functions. In environments with both spatial and temporal coupling requirements, we outperform prior methods and provides intuitive visualizations of its skill switching strategy.

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IROS Conference 2017 Conference Paper

COCrIP: Compliant OmniCrawler in-pipeline robot

Akash Singh
Enna Sachdeva
Abhishek Sarkar
K. Madhava Krishna

This paper presents a modular in-pipeline climbing robot with a novel compliant foldable OmniCrawler mechanism. The robot has a series of 3 compliant foldable OmniCrawler modules interconnected by links via passive joints. The circular cross-section of the module enables a holonomic motion to facilitate the alignment of the robot in the direction of bends. Additionally, the crawler mechanism provides a fair amount of traction, even on slippery pipe surfaces. These advantages of crawler modules have been further augmented by incorporating active compliance in the module, which helps to negotiate sharp bends in small diameter pipes. Introducing compliance in the crawler module with a single chain-lugs assembly is the the key novelty of this design. For the desirable pipe diameter and curvature of the bends, the spring stiffness value for each passive joint is determined by formulating a constrained optimization problem using the quasi-static model of the robot. Moreover, a minimum friction coefficient value between the module-pipe surface which can be vertically climbed by the robot without slipping is estimated. The numerical simulation results have further been validated by experiments on real robot prototype.

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