About

Satyandra K. Gupta holds the Smith International Professorship in Mechanical Engineering and is a Professor of Aerospace and Mechanical Engineering and Computer Science at USC. His research focuses on areas related to mechanical engineering, aerospace, and computer science, contributing to advancements in these fields. As a distinguished faculty member, he is recognized for his expertise and leadership in engineering and computer science disciplines.

Research topics

• Computer Science
• Artificial Intelligence
• Engineering
• Mathematics
• Machine Learning
• Computer vision
• Mechanical engineering
• Geometry
• Operating system
• Engineering drawing
• Control engineering
• Real-time computing
• Structural engineering
• Simulation
• Algorithm

Selected publications

• A hierarchical approach to imitation learning for manipulation tasks requiring time varying forces

  Robotics and Computer-Integrated Manufacturing · 2026-04-12

  articleSenior author
  PublisherDOI

• Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing

  arXiv (Cornell University) · 2026-03-08

  articleOpen accessSenior author

  Robotic bin packing is widely deployed in warehouse automation, with current systems achieving robust performance through heuristic and learning-based strategies. These systems must balance compact placement with rapid execution, where selecting alternative items or reorienting them can improve space utilization but introduce additional time. We propose a selection-based formulation that explicitly reasons over this trade-off: at each step, the robot evaluates multiple candidate actions, weighing expected packing benefit against estimated operational time. This enables time-aware strategies that selectively accept increased operational time when it yields meaningful spatial improvements. Our method, STEP (Space-Time Efficient Packing), uses a preference-conditioned, Transformer-based reinforcement learning policy, and allows generalization across candidate set sizes and integration with standard placement modules. It achieves a 44% reduction in operational time without compromising packing density. Additional material is available at https://step-packing.github.io.

  PublisherOA PDF

• Preference-Conditioned Reinforcement Learning for Space-Time Efficient Online 3D Bin Packing

  Open MIND · 2026-03-08

  preprintSenior author

  Robotic bin packing is widely deployed in warehouse automation, with current systems achieving robust performance through heuristic and learning-based strategies. These systems must balance compact placement with rapid execution, where selecting alternative items or reorienting them can improve space utilization but introduce additional time. We propose a selection-based formulation that explicitly reasons over this trade-off: at each step, the robot evaluates multiple candidate actions, weighing expected packing benefit against estimated operational time. This enables time-aware strategies that selectively accept increased operational time when it yields meaningful spatial improvements. Our method, STEP (Space-Time Efficient Packing), uses a preference-conditioned, Transformer-based reinforcement learning policy, and allows generalization across candidate set sizes and integration with standard placement modules. It achieves a 44% reduction in operational time without compromising packing density. Additional material is available at https://step-packing.github.io.

  DOI

• Energy-Aware Planning for Legged Robot Performing Logistics Tasks in Agriculture Applications

  ASME Letters in Translational Robotics · 2026-04-27

  articleSenior author

  Abstract Legged robots can significantly increase human productivity by performing delivery tasks, especially in unstructured agriculture fields. In large outdoor environments, legged robots typically operate independently from tethered power sources, relying on onboard batteries. If a robot runs out of energy while executing a task, it will require human intervention, resulting in delays. On the other hand, frequent battery recharging or replacement could prolong task completion times. This paper presents a systematic framework to enhance productivity for logistic tasks. The framework features a map construction utility, an energy consumption model to measure battery usage, and an energy-aware hierarchical planning approach that accounts for energy consumption and integrates appropriate battery replacement strategies to ensure that tasks are completed efficiently. Our algorithm first generates different scenarios, considering battery replacement options, payload partitioning, and speed reduction strategies. Subsequently, it employs graph search methods to identify the optimal plan that minimizes delivery completion time. We illustrate the effectiveness of our planning approach on a terrain with varying slopes and delivery tasks with different requirements. We also demonstrated that our robot can successfully traverse in narrow furrows in broccoli and cabbage farms.

  PublisherDOI

• 2026 Roadmap on Artificial Intelligence and Machine Learning for Smart Manufacturing

  arXiv (Cornell University) · 2026-04-05

  preprintOpen access

  The evolution of artificial intelligence (AI) and machine learning (ML) is reshaping smart manufacturing by providing new capabilities for efficiency, adaptability, and autonomy across industrial value chains. However, the deployment of AI and ML in industrial settings still faces critical challenges, including the complexity of industrial big data, effective data management, integration with heterogeneous sensing and control systems, and the demand for trustworthy, explainable, and reliable operation in high-stakes industrial environments. In this roadmap, we present a comprehensive perspective on the foundations, applications, and emerging directions of AI and ML in smart manufacturing. It is structured in three parts. The first highlights the foundations and trends that frame the evolution of AI in smart manufacturing. The second focuses on key topics where AI is already enabling advances, including industrial big data analytics, advanced sensing and perception, autonomous systems, additive and laser-based manufacturing, digital twins, robotics, supply chain and logistics optimization, and sustainable manufacturing. The third section explores non-traditional ML approaches that are opening new frontiers, such as physics-informed AI, generative AI, semantic AI, advanced digital twins, explainable AI, RAMS, data-centric metrology, LLMs, and foundation models for highly connected and complex manufacturing systems. By identifying both opportunities and remaining barriers across these areas, this roadmap outlines the advances needed in methods, integration strategies, and industrial adoption. We hope this roadmap will serve as a guide for researchers, engineers, and practitioners to accelerate innovation, align academic and industrial priorities, and ensure that AI-driven smart manufacturing delivers reliable, sustainable, and scalable impact for the future of manufacturing ecosystems.

  PublisherDOI

• Compliant Mechanism for Robotic In-Space Assembly and Learning-Based Safety Limit Detection

  2026-01-08

  articleSenior author

  Autonomous in-space assembly requires mechanisms that can tolerate multi-axis misalignment without relying on active impedance control or large alignment features. This paper presents the design, fabrication, and experimental validation of a compact passive Remote Center of Compliance (RCC) device tailored for the constraints of in-space robotic assembly. The RCC employs space-qualified elastomeric elements arranged in a configuration to provide passive translational and rotational compliance, enabling correction of misalignment while maintaining low structural and control complexity. Bench-top experiments using a 7-DOF robotic arm demonstrate that the RCC accommodates lateral, pitch, and roll misalignments of up to 9 mm, 10°, and 14°, respectively, while reducing insertion forces and torques by 34-89% compared to a rigid end-effector. Because passive mechanisms provide no internal state feedback, we further develop a learning-based method for estimating RCC deflection and for detecting proximity to mechanical limits using short windows of force–torque and relative-pose data. Static stiffness and compliance models are shown to be insufficient due to strong nonlinearities and hysteresis, motivating a temporal convolutional network which infers full six-degree-of-freedom deflection. The model achieves normalized mean absolute errors of 0.14-0.18 and reliably classifies limit proximity using thresholded normalized displacement labels. Confusion matrices show high neutrality recall and strong directional consistency, with cross-sign errors below 10%, enabling conservative, low-latency limit detection suitable for real-time operation. Together, the proposed RCC device and learning-based safety-limit estimator provide a low-mass, low-power, and sensing-lightweight solution for misalignment-tolerant robotic assembly in the resource-constrained conditions of space environments.

  PublisherDOI

• Physical Artificial Intelligence for Powering the Next Revolution in Robotics

  Journal of Computing and Information Science in Engineering · 2025-10-10 · 5 citations

  articleSenior author

  Abstract Physical artificial intelligence (AI) is driving the next revolution in robotics by grounding perception, action, and cognition within a robot’s physical structure. Unlike traditional systems that rely on disembodied reasoning and preprogrammed control, physical AI leverages sensorimotor coupling to enable real-time adaptation, experiential learning, and generalized task performance. Advances in machine learning, high-fidelity simulations, and multimodal sensing have accelerated progress toward real-world deployment. This position article articulates a unifying perspective on physical AI, outlining its conceptual evolution, defining system-level principles, and analyzing key functional subsystems, such as situational awareness, mapping, planning, control, and human–robot interaction. It provides a domain-wise readiness assessment across manufacturing, healthcare, logistics, agriculture, service robotics, and space exploration, highlighting opportunities and limitations. Finally, it identifies critical challenges—real-time performance, cybersecurity, benchmarking, safety, interpretability, and energy efficiency—and proposes codesign principles and evaluation frameworks to guide future research. By synthesizing these elements, the article positions physical AI as a foundational paradigm for trustworthy, adaptive, and mission-ready robotic systems, offering readers a roadmap for research priorities, cross-domain insights, and practical implications that will shape the next era of robotics.

  PublisherDOI

• Task-Context-Aware Diffusion Policy with Language Guidance for Multi-task Disassembly

  2025-08-17

  articleSenior author

  Diffusion-based policy learning has shown strong performance across diverse robotic tasks, often achieving high success rates. However, real-world deployment requires more than task success—it demands efficient execution and the ability to handle complex environments. In many assembly and disassembly settings, a single scene contains multiple potential task goals. This can confuse learned policies, leading to ambiguous behavior. Enabling explicit task selection via natural language is thus crucial for robust and flexible operation. In this paper, we address two key challenges: (1) improving task execution efficiency by structuring tasks into distinct sub-task modes using language, and (2) resolving goal ambiguity by allowing human operators to specify desired tasks through natural language commands. We further introduce an adaptive parameter selection mechanism that adjusts reliance on different sensory modalities depending on the active sub-task. We evaluate our approach on the NIST Task Board, a representative benchmark with multiple co-located task goals. Our method improves execution speed by 57% and increases task success rate by 19% compared to baseline approaches. Demonstration videos are available at: https://rros-lab.github.io/task-aware-diffusion/.

  PublisherDOI

• Embodied AI for Smart Robotic Cells in Manufacturing Applications

  Proceedings of the AAAI Conference on Artificial Intelligence · 2025-04-11 · 1 citations

  articleOpen access1st authorCorresponding

  Many manufacturing companies are facing an acute shortage of qualified workers. Deploying robotic cells is a potential solution to address this challenge. Historically robots have been deployed only in mass production applications in manufacturing. A large fraction of manufacturing is classified as high-mix manufacturing where a large variety of products are produced. Manually programming robots is not a viable solution in high-mix manufacturing applications. Robotic cells need to be powered by embodied AI to make them useful in high-mix manufacturing applications. This paper aims to build a bridge between smart manufacturing and AI communities to enable AI researchers to develop methods and tool that can be successfully deployed to realize smart robotic cells for high-mix manufacturing applications. This paper highlights key requirements for developing embodied AI for powering robotic cells for high-mix manufacturing applications. It also makes the case for approaches that combine model-based and data-driven methods to meet the needs of embodied AI in manufacturing applications and describes the role of generative AI approaches in smart manufacturing applications. Finally, it describes how AI can be used to enhance digital twins and augment human-machine interfaces in manufacturing applications.

  PublisherOA PDFDOI

• Proactive Contingency-Aware Task Allocation and Scheduling in Multi-Robot Multi-Human Cells via Hindsight Optimization

  IEEE Transactions on Automation Science and Engineering · 2025-01-01 · 2 citations

  articleSenior author

  Multi-robot systems are becoming more common in various real-world applications, such as manufacturing and warehouse logistics. However, task allocation and scheduling for a multi-agent team face complex challenges due to the need to simultaneously consider time-extended tasks, task constraints, and uncertainties in execution. Potential task failures or contingencies can add additional tasks to recover from the failures, and reactively addressing contingencies can decrease teaming efficiency. To efficiently and proactively consider contingencies, this paper proposes treating the problem as a multi-robot task allocation under uncertainty problem. We suggest a hierarchical approach that divides the problem into two layers. We use mathematical program formulation for the lower layer to find the optimal solution for a deterministic multi-robot task allocation problem with known task outcomes. The higher-layer search intelligently generates more likely combinations of contingency scenarios and calls the inner-level search repeatedly to find the optimal task allocation sequence for the given scenario. We validate our results in simulation for manufacturing applications and demonstrate that our method can reduce the effect of potential delays from contingencies.Note to Practitioners—Automation engineers interested in deploying robotic cells in low-volume applications need to consider contingency handling. When the occurrence of contingencies can be characterized as probability distributions, it is often useful to consider using a proactive approach for task allocation and scheduling. To implement our algorithm, automation engineers will need to develop a hierarchical task network specified by domain experts that models task constraints and a task-agent duration model, which may be generated from simulation environments. Furthermore, they must identify tasks that can result in contingencies and describe them with a probabilistic model. This model can be generated from historical data and/or real-world experiments. Lastly, for addressing the contingency, the practitioner will need to specify a task procedure to recover from a specific contingency type. To run the algorithm, we found that repeatedly approximating the best proactive task allocation for a fixed computation budget and dispatching the best tasks worked well. The computation budget required to approximate the best task allocation is directly affected by the number of contingency scenarios that can be sampled. Therefore, the practitioner must determine a suitable computational budget empirically based on the number of contingencies that can occur.

  PublisherDOI

Recent grants

• Collaborative Research: Manufacturing of Mesoscopic 3D Articulated Devices Using Robomold Tooling

  NSF · $229k · 2005–2009

• PECASE: Automated Design of Multi-Piece Molds -- A Step towards Manufacturing of Geometrically Complex Heterogeneous Objects

  NSF · $393k · 2001–2007

• NRI: Collaborative Research: Enabling Risk-Aware Decision Making in Human-Guided Unmanned Surface Vehicle Teams

  NSF · $526k · 2016–2019

• Collaborative Research: Computational Foundations for Learning, Verifying, and Applying Model Simplification Rules

  NSF · $76k · 2016–2017

• Collaborative Research: Automatic Generation of Context-Dependent Simplified Models to Support Interactive Virtual Assembly

  NSF · $243k · 2007–2012

Frequent coauthors

• Hugh A. Bruck

  University of Maryland, College Park

  75 shared

• Dana S. Nau

  University of Maryland, College Park

  44 shared

• Rishi K. Malhan

  University of Southern California

  40 shared

• Brual C. Shah

  University of Southern California

  39 shared

• Ariyan M. Kabir

  University of Southern California

  36 shared

• Prahar M. Bhatt

  University of Southern California

  33 shared

• Krishnanand N. Kaipa

  Norfolk State University

  32 shared

• Jason M. Gregory

  32 shared

Labs

• Artificial Intelligence, Machine Learning, Privacy/FATEPI

Education

• Ph.D., Computer Science

  University of California, Los Angeles

  1986

• M.S., Computer Science

  University of California, Los Angeles

  1983

• B.S., Computer Science and Engineering

  Indian Institute of Technology, Kanpur

  1981

Awards & honors

• Young Investigator Award from the Office of Naval Research (…
• Robert W. Galvin Outstanding Young Manufacturing Engineer Aw…
• CAREER Award from the National Science Foundation (2001)
• Presidential Early Career Award for Scientists and Engineers…
• Invention of the Year Award at the University of Maryland (2…