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IRIM Seminar Series

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Now showing 1 - 10 of 142
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Hardware Design and Control Algorithms for Agile and Versatile Legged Robots

2025-01-22 , Park, Hae-Won

Biological animals excel at navigating complex and challenging environments, leveraging their hardware to perform dynamic motions and athletic skills that overcome diverse obstacles. Despite recent advancements, robotic systems still lack comparable dynamic locomotion capabilities. In this talk, I will present our lab's efforts to bridge this gap by developing robust hardware and effective control algorithms that enable both agility and robustness in legged robots. I will begin by introducing our quadruped robot platforms: HOUND, designed for high-speed locomotion on complex terrains, and MARVEL, designed for agile and versatile climbing. HOUND incorporates custom electric actuators, while MARVEL uses magnetic feet to generate climbing force. I will then discuss the control algorithms that drive these robots, leveraging model predictive control and reinforcement learning techniques. Finally, I will present our latest learning-based locomotion control framework, capable of synthesizing and executing diverse dynamic motions across various terrains. This framework combines a low-level skill policy, pre-trained using a large offline dataset generated via trajectory optimization, with a reinforcement learning policy trained on diverse terrains. With this integrated approach, HOUND achieves speeds of up to 9.5 m/s, making it the fastest legged robot, while MARVEL can traverse ceilings and vertical walls at speeds of up to 0.5 m/s and 0.7 m/s, respectively.

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Regenstein Center for Bionic Medicine: Intuitive Control of Bionic Limbs

2024-04-10 , Hargrove, Levi

Amputation is a leading cause of disability, and prosthetic devices are commonly accepted treatment options to restore functional capabilities. However, current prosthetic devices still cannot fully match the functionality of their natural counterparts. This talk focuses on the progress made in the development and control of bionic limbs for individuals with limb loss. The first portion of the talk provides an overview of the development, testing and commercialization of pattern recognition control systems for prosthetic arms, including their operation with advanced surgical techniques, such as targeted muscle reinnervation. A significant emphasis of this work has been on evaluation based on real user feedback, ensuring that the developed technologies meet the actual needs and preferences of end users. The second portion focuses on the application of these approaches (ie statistical pattern recognition and finite state-machines) to controlling powered leg prostheses. Finally, I will discuss our recent work in using deep-learning coupled with benchmark datasets (some collected at Georgia Tech) to remove the reliance of finite-state machines from our overall control approach.

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Control Principles for Robot Learning

2024-02-07 , Murphey, Todd

Embodied learning systems rely on motion synthesis to enable efficient and flexible learning during continuous online deployment. Motion motivated by learning needs can be found throughout natural systems, yet there is surprisingly little known about synthesizing motion to support learning for robotic systems. Learning goals create a distinct set of control-oriented challenges, including how to choose measures as objectives, synthesize real-time control based on these objectives, impose physics-oriented constraints on learning, and produce analyses that guarantee performance and safety with limited knowledge. In this talk, I will discuss learning tasks that robots encounter, measures for information content of observations, and algorithms for generating action plans. Examples from biology and robotics will be used throughout the talk and I will conclude with future challenges.

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Dynamic Legged LocoManipulation: Balancing Reinforcement Learning with Model-Based Control

2023-04-12 , Sreenath, Koushil

Model-based control methods such as control Lyapunov and control barrier functions can provide formal guarantees of stability and safety for dynamic legged locomotion, given a precise model of the system. In contrast, learning-based approaches such as reinforcement learning have demonstrated remarkable robustness and adaptability to model uncertainty in achieving quadrupedal locomotion. However, reinforcement learning based policies lack formal guarantees, which is a known limitation. In this presentation, I will demonstrate that simple techniques from nonlinear control theory can be employed to establish formal stability guarantees for reinforcement learning policies. Moreover, I will illustrate the potential of reinforcement learning for more complex bipedal and humanoid robots, as well as for loco-manipulation tasks that entail both locomotion and manipulation. This brings up an intriguing question: Is reinforcement learning alone sufficient for achieving optimal results in dynamic legged locomotion, or is there still a need for model-based control methods?

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Robots that know when they don't know

2025-01-08 , Majumdar, Anirudha

Foundation models from machine learning have enabled rapid advances in perception, planning, and natural language understanding for robots. However, current systems lack any rigorous assurances when required to generalize to novel scenarios. For example, perception systems can fail to identify or localize unfamiliar objects, and large language model (LLM)-based planners can hallucinate outputs that lead to unsafe outcomes when executed by robots. How can we rigorously quantify the uncertainty of machine learning components such that robots know when they don’t know and can act accordingly? In this talk, I will present our group’s work on developing principled theoretical and algorithmic techniques for providing formal assurances on learning-enabled robots that act based on rich sensory inputs (e.g., vision) and natural language instructions. The key technical insight is to leverage and extend powerful methods from conformal prediction and generalization theory for rigorous uncertainty quantification in a way that complements and scales with the growing capabilities of foundation models. I will present experimental validation of our methods for providing strong statistical guarantees on LLM planners that ask for help when they are uncertain, and for vision-based navigation and manipulation.

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A symbiotic philosophy for bio-inspired robotics

2024-03-27 , Moore, Talia

Humans have frequently looked to natural phenomena to inspire the design of art, structures, and mechanisms. However, there are as many different ways to learn from nature as there are words for this approach: bioinspiration, biomimicry, and biodesign to name a few. In this talk, I propose a taxonomy for categorizing distinct biodesign approaches and use examples from my own research to illustrate the methodology and benefits of each. In particular, I introduce the field of Animal-Robot Interactions and describe how bio-inspired approaches can be used to further biological inquiry while advancing robotics.

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Robotic Locomotion and Sensing on Deformable Terrains

2024-01-24 , Qian, Feifei

Achieving robust mobility on natural and deformable terrains is pivotal for robots to operate effectively in real-world scenarios. Despite remarkable progress in robotics hardware and software, today’s robots still face challenges in traversing terrains like sand dunes, soft snow, and sticky mud, significantly trailing behind the locomotion abilities of animals and humans. This gap limits robots’ capabilities to aid in critical missions such as earthquake search and rescue, supply delivery, and planetary exploration. This talk discusses our recent efforts to bridge this gap. First, we show that by understanding the force responses from deformable terrains, we could allow robots to elicit desired ground reaction forces from challenging terrains like sand and mud and produce significantly improved locomotion performance. Second, we show that by leveraging the high force transparency of direct-drive actuators, robots could use their legs as proprioceptive sensors to determine substrate strength and mechanical properties. This proprioceptive sensing capability can enable robots to gather rich information from their environment during every step, and adapt their locomotion strategies accordingly. Finally, we discuss our latest progress in applying these locomotion and sensing strategies in earth and planetary exploration scenarios, and how the improved sensing and locomotion capabilities pave the way for new human-robot teaming workflows.

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Unifying Semantic and Physical Intelligence for Generalist Humanoid Robots

2024-09-04 , Shi, Guanya

Humanoid robots offer two unparalleled advantages in general-purpose embodied intelligence. First, humanoids are built as generalist robots that can potentially do all the tasks humans can do in complex environments. Second, the embodiment alignment between humans and humanoids allows for the seamless integration of human cognitive skills with versatile humanoid capabilities. To build generalist humanoids, there are three critical aspects of intelligence: (1) Semantic intelligence (how the robot understands the world and reasons); (2) Physical/Motion intelligence (locomotion and manipulation skills); and (3) Mechanical/Hardware intelligence (how the robot actuates and senses). In this talk, I will present some recent works (H2O, OmniH2O, WoCoCo, ABS) that aim to unify semantic and physical intelligence for humanoid robots. In particular, H2O and OmniH2O provide a universal and dexterous interface that enables diverse human control (e.g., VR, RGB) and autonomy (e.g., using imitation learning or VLMs) methods for humanoids, WoCoCo provides an efficient framework for loco-manipulation skill learning without motion priors, and ABS provides safety guarantees for agile vision-based locomotion control. Finally, I will briefly discuss how to combine learning-based control approaches and traditional model-based control approaches to get the best of two worlds.

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From State Space Control to Intelligent Machines: A Five-Decade Journey in Mechanical Systems Control

2024-03-06 , Tomizuka, Masayoshi

Masayoshi Tomizuka joined the Mechanical Engineering Department of UC Berkeley in 1974 after obtaining a PhD from MIT in the same year. It was an exciting time for someone in the field of dynamic systems and control. The 1960’s – 1970’s was the period when the state space control theories blossomed such as maximum principle, dynamic programming, Lyapunov stability, Kalman filtering, Linear Quadratic Gaussian Control and stability based adaptive control theory. At the same time, computer/information technology has made phenomenal advances during the period. At MIT Tomizuka used IBM1130 (with a card reader and printer) and a PDP-8 mini-computer. When he joined UC Berkeley, the campus mainframe computer was a CDC (Control Data Corporation) 6000 series computer, and the lab computer was PDP-7, which was upgraded to PDP-11, LSI-11, etc. The control program at Berkeley covered from both theory to implementation, and it was followed by many other schools. The1970’s was the time when a new generation of mechanical systems showed up; IBM introduced the Winchester Hard Disk Drive in 1973 and the FANUC Corporation was established in 1972. Tomizuka's laboratory, Mechanical Systems Control (MSC) Laboratory, naturally evolved to a group to study both mathematical and implementation aspects of controls. The current research emphasis of the MSC Laboratory is on intelligent industrial robots and autonomous driving. Several representative current projects will be introduced.

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Do We Really Need all that Data? Learning and Control for Contact-rich Manipulation

2023-11-15 , Posa, Michael

For all the promise of big-data machine learning, what will happen when robots deploy to our homes and workplaces and inevitably encounter new objects, new tasks, and new environments? If a solution to every problem cannot be pre-trained, then robots will need to adapt to this novelty. Can a robot, instead, spend a few seconds to a few minutes gathering information and then accomplish a complex task? Why does it seem that so much data is required, anyway? I will first argue that the hybrid or contact-driven aspects of manipulation clashes with the inductive biases inherent in standard learning methods, driving this current need for large data. I will then show how contact-inspired implicit learning, embedding convex optimization, can reshape the loss landscape and enable more accurate training, better generalization, and ultimately data efficiency. Finally, I will present our latest results on how these learned models can be deployed via real-time multi-contact MPC for robotic manipulation.

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