Author name cluster

Xifeng Gao

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7 papers

2 author rows

IROS Conference 2025 Conference Paper

ChatBuilder: LLM-assisted Modular Robot Creation

Xin Chen
Xifeng Gao
Lifeng Zhu
Aiguo Song
Zherong Pan

Modular robotic structures simplify robot design and manufacturing by using standardized modules, enhancing flexibility and adaptability. However, the need for manual input in design and assembly limit their potential. Current methods to automate this process still require significant human effort and technical expertise. This paper introduces a novel approach that employs Large Language Models (LLMs) as intelligent agents to automate the creation of modular robotic structures. We decompose the modular robot creation task and develop two agents based on LLM to plan and assemble the modular robots from text prompts. By inputting a textual description, users can generate robot designs that are validated in both simulated and real-world environments. This method reduces the need for manual intervention and lowers the technical barrier to creating complex robotic systems.

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AAAI Conference 2024 Conference Paper

Learning Reduced Fluid Dynamics

Zherong Pan
Xifeng Gao
Kui Wu

Predicting the state evolution of ultra high-dimensional, time-reversible fluid dynamic systems is a crucial but computationally expensive task. Existing physics-informed neural networks either incur high inference cost or cannot preserve the time-reversible nature of the underlying dynamics system. We propose a model-based approach to identify low-dimensional, time reversible, nonlinear fluid dynamic systems. Our method utilizes the symplectic structure of reduced Eulerian fluid and use stochastic Riemann optimization to obtain a low-dimensional bases that minimize the expected trajectory-wise dimension-reduction error over a given distribution of initial conditions. We show that such minimization is well-defined since the reduced trajectories are differentiable with respect to the subspace bases over the entire Grassmannian manifold, under proper choices of timestep sizes and numerical integrators. Finally, we propose a loss function measuring the trajectory-wise discrepancy between the original and reduced models. By tensor precomputation, we show that gradient information of such loss function can be evaluated efficiently over a long trajectory without time-integrating the high-dimensional dynamic system. Through evaluations on a row of simulation benchmarks, we show that our method reduces the discrepancy by 50-90 percent over conventional reduced models and we outperform PINNs by exactly preserving the time reversibility.

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IROS Conference 2023 Conference Paper

Learning Reduced-Order Soft Robot Controller

Chen Liang
Xifeng Gao
Kui Wu 0003
Zherong Pan

Deformable robots are notoriously difficult to model or control due to its high-dimensional configuration spaces. Direct trajectory optimization suffers from the curse-of-dimensionality and incurs a high computational cost, while learning-based controller optimization methods are sensitive to hyper-parameter tuning. To overcome these limitations, we hypothesize that high fidelity soft robots can be both simulated and controlled by restricting to low-dimensional spaces. Under such assumption, we propose a two-stage algorithm to identify such simulation- and control-spaces. Our method first identifies the so-called simulation-space that captures the salient deformation modes, to which the robot's governing equation is restricted. We then identify the control-space, to which control signals are restricted. We propose a multi-fidelity Riemannian Bayesian bilevel optimization to identify task-specific control spaces. We show that the dimension of control-space can be less than 10 for a high-DOF soft robot to accomplish walking and swimming tasks, allowing low-dimensional MPC controllers to be applied to soft robots with tractable computational complexity.

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IROS Conference 2021 Conference Paper

Decentralized, Unlabeled Multi-Agent Navigation in Obstacle-Rich Environments using Graph Neural Networks

Xuebo Ji
He Li
Zherong Pan
Xifeng Gao
Changhe Tu

We propose a decentralized, learning-based solution to the challenging problem of unlabeled multi-agent navigation among obstacles, where robots need to simultaneously tackle the problems of goal assignment, local collision avoidance, and navigation. Our method has each robot infer their desired action by communicating with each other as well as a set of position-fixed routers. The inference is carried out on a graph neural network (GNN) with both robot and router nodes. We train our GNN using imitation learning on a small group of robots, where we modify the centralized version of the concurrent goal assignment and planning algorithm (CAPT) as our expert. By sharing weights among all robots and routers, our model can scale to unseen environments with any number of possibly kinodynamic agents during test time. We have achieved a success rate of 91. 2% and 85. 6% for point and car-like robots, respectively. Source code will be publicly available upon the publication of the work.

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ICRA Conference 2021 Conference Paper

Robust & Asymptotically Locally Optimal UAV-Trajectory Generation Based on Spline Subdivision

Ruiqi Ni
Teseo Schneider
Daniele Panozzo
Zherong Pan
Xifeng Gao

Generating locally optimal UAV-trajectories is challenging due to the non-convex constraints of collision avoidance and actuation limits. We present the first local, optimization-based UAV-trajectory generator that simultane-ously guarantees validity and asymptotic optimality for known environments. Validity: Given a feasible initial guess, our algo-rithm guarantees the satisfaction of all constraints throughout the process of optimization. Asymptotic Optimality: We use an asymptotic exact piecewise approximation of the trajectory with an automatically adjustable resolution of its discretization. The trajectory converges under refinement to the first-order stationary point of the exact non-convex programming problem. Our method has additional practical advantages including joint optimality in terms of trajectory and time-allocation, and robustness to challenging environments as demonstrated in our experiments.

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ICRA Conference 2020 Conference Paper

Grasping Fragile Objects Using A Stress-Minimization Metric

Zherong Pan
Xifeng Gao
Dinesh Manocha

We present a new method to generate optimal grasps for brittle and fragile objects using a novel stress- minimization (SM) metric. Our approach is designed for objects that are composed of homogeneous isotopic materials. Our SM metric measures the maximal resistible external wrenches that would not result in fractures in the target objects. In this paper, we propose methods to compute our new metric. We also use our SM metric to design optimal grasp planning algorithms. Finally, we compare the performance of our metric and conventional grasp metrics, including Q 1, Q ∞, Q G11, Q MSV, Q VEW. Our experiments show that our SM metric takes into account the material characteristics and object shapes to indicate the fragile regions, where prior methods may not work well. We also show that the computational cost of our SM metric is on par with prior methods. Finally, we show that grasp planners guided by our metric can lower the probability of breaking target objects.

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IROS Conference 2020 Conference Paper

Inner-Approximation of Manipulable and Reachable Regions using Bilinear Matrix Inequalities

Zherong Pan
Liang He 0008
Xifeng Gao

Given an articulated robot arm, we present a method to identify two regions with non-empty interiors. The first region is a subset of the configuration space where every point in the region is manipulable. The second region is a subset of the workspace where every point in the region is reachable by the end-effector. Our method expresses the kinematic state of the robot arm using the maximal coordinates, so that the kinematic constraints take polynomial forms. We then reformulate the optimization-based inverse kinematics (IK) algorithm as gradient flows. Finally, we use sum-of-squares (SOS) programming to certify the convergence of each gradient flow. Our main result shows that the feasibility of an SOS programming problem is a sufficient condition for the manipulability and reachability of the sublevel sets of polynomial functions. Our method can be used to certify manipulable or reachable regions by solving a set of linear matrix inequalities (LMIs) or to maximize the volume of a region by solving a set of bilinear matrix inequalities (BMIs). These identified regions can then be used in various motion planning problems as hard safety constraints.

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