Robust Nonprehensile Object Transport with Quadruped Robots
Authors: Ainoor Teimoorzadeh, Riccardo Pretto, Mario Selvaggio, Gokhan Alcan, Sami Haddadin
Organizations: Munich Institute of Robotics & Machine Intelligence, Technical University of Munich (TUM), Munich, Germany · Tampere University, Finland · PRISMA Lab, Department of Electrical Engineering and Information Technology, University of Naples Federico II, Via Claudio 21, 80125, Naples, Italy · Mohamed Bin Zayed University of Artificial Intelligence, Masdar City, Abu Dhabi, UAE
In this paper, we present a robust nonprehensile object transportation framework for quadruped robots. An uncertainty-aware trajectory optimization method generates object motions with minimal closed-loop sensitivity to uncertain parameters. The resulting reference trajectory is tracked using a coupled convex model predictive controller that jointly predicts the CoM dynamics of the quadruped and the payload followed by a whole-body QP that enforces ground reaction constraints. The approach is evaluated through extensive simulations and real-world experiments under variations in the object's inertial parameters. Its performance is compared with fixed-orientation and straight-line trajectories as baseline. The results show that the optimized object motion reduces the sliding by approximately 50% compared with the fixed-orientation baseline and 30% compared with the straight-line baseline, while also achieving lower robot CoM tracking errors.
Figures & tables
Fig. 1: Picture of the problem addressed in this paper: A quadruped carries an unrestrained object on its back mounted flat platform from the initial point A to the final point B over an optimized sensitivity-aware trajectory planned for the reference frame {B} attached to the object.
Fig. 2: Schematic of the tray-based nonprehensile transportation model, in which a cylindrical object rests on a platform mounted on the back of a quadrupedal robot. The symbols are defined in Sec. II-B .
Fig. 3: Conceptual block scheme of the proposed control architecture. The sensitivity-based trajectory generator provides the desired object pose, twist, and its derivative to the coupled robot-object convex MPC. Using these references and the estimated robot and object states, the MPC computes the desired ground reaction forces and base accelerations. The whole-body controller maps these commands and the measured generalized state into the joint torque command applied to the quadruped.
Object CoM height
zb
0.35≤zb≤0.40m
Forward velocity
vb,x
∣vb,x∣≤1.0[m/s]
Lateral velocity
vb,y
∣vb,y∣≤0.05[m/s]
Vertical velocity
vb,z
∣vb,z∣≤0.2[m/s]
Control Parameters
Kp = diag (250,15)
Kd = diag (15,0.2)
TABLE I: Trajectory generation constraints and control parameters
Fig. 4: 3D visualization of the optimized robust object trajectories over a 5 s horizon for travel distances of 2 , 2.2 , and 2.4 m, shown in black, gray, and light gray, respectively. The object orientations are illustrated at intervals of 0.25 along the travel distance.
Fig. 5: Time evolution of a representative Λ for the optimized trajectories with travel distances of 2 , 2.2 , and 2.4 m. The thick solid curves represent the nominal solutions, the thin curves show the Monte Carlo realizations under ±10% perturbations of the object inertial parameters, and the dashed curves indicate the corresponding envelopes.
Fig. 6: Monte Carlo comparison of the optimized robust (top), fixed-orientation (middle), and straight-line (bottom) trajectories performed in the MuJoCo simulation environment. Each row shows representative snapshots and (a) robot CoM tracking error, (b) object RMS translation, and (c) object RMS orientation deviation. Blue, orange, and green denote 2 , 2.2 , and 2.4 m, respectively; error bars show the variation across realizations.
Fig. 7: Monte Carlo evaluation of the optimized trajectories under friction variations, with 30 realizations per coefficient.
Fig. 8: Time evolution of the object sliding for (a) the optimized trajectory and (b) straight-line baseline. The dashed curves represent the nominal case, while the dark and light curves correspond to +10% and −10% variations in the nominal object mass, respectively.
Quadruped robots are increasingly expected to carry objects while moving through human environments. But what happens when a person interacts directly with the payload rather than with the robot? If the payload is unrestrained, the robot must distinguish intentional external interactions from ordinary payload motion, while still keeping the load balanced and maintaining stable locomotion. How can a quadruped infer and compliantly respond to such interactions using only onboard measurements? In this work, we develop a force-aware locomotion framework that treats payload interactions as commands that shape the motion of the combined robot-payload system. Our approach separates the learning of force-aware locomotion and force estimation on an unrestrained payload. We combine a compliant load-carrying policy with a causal force estimator, trained through estimator-in-the-loop data aggregation and finetuning, to predict interactions from onboard robot measurements. Our simulations and real-world experiments show that the resulting controller can maintain stable payload-carrying locomotion, yield compliantly to external interactions, and use the inferred force to support human-guided changes in the robot's trajectory.
Shaunak A. Mehta, Mayank Mishra, Prajit KrisshnaKumar +2
Fujitsu Research of America, Pittsburgh, PA, 15213.
In this letter, we present a hierarchical control framework that enables wheeled bipedal robots to perform planar object sliding tasks with their wheeled legs. The proposed approach formulates a nonlinear model predictive controller (NMPC) based on a reduced-order three rigid bodies (TRB) dynamical model that explicitly accounts for the hip roll degree of freedom and multiple wheel-environment contact modes, which is essential for lateral stepping and pedipulation tasks. Within this framework, the NMPC simultaneously regulates robot locomotion and interaction forces, allowing the robot to stably execute both rolling and object manipulation behaviors. A trajectory-optimization-based robot-object motion planner is developed to generate reference motions that incorporate stick-slip transitions in ground-object contact. Two representative pedipulation motions, namely scooting and lateral sliding, are validated through real-world hardware experiments, in which the robot successfully retrieves a 1 kg object from under a desk and slides a 4 kg object over a distance of 0.228 m via scooting.
Yue Qin, Yulun Zhuang, Zelin Shen +1
Department of Robotics, University of Michigan, Ann Arbor, MI 48109, USA
Transporting unsecured payloads with legged robots over uneven terrain requires balancing locomotion performance and payload stability, since aggressive motion can destabilize the payload even when the robot remains stable. We study quadrupedal transportation of unsecured stacked boxes on an edgeless torso-mounted board without dedicated payload sensors or active carrier mechanisms. To address this trade-off, we propose Payload-Adaptive Multi-Objective Reinforcement learning for Transportation (PAMORT). PAMORT trains a multi-objective base policy conditioned on a preference vector that weights locomotion and payload-stability reward groups, then trains a weight adjuster on the frozen policy to adapt this preference online from proprioception. In simulation, PAMORT achieves comparable or better overall transportation success than a corresponding single-objective baseline across different payload configurations, including an unseen three-box stack, despite training only with two boxes. Real-world experiments on a Unitree Go2 demonstrate zero-shot transfer to slopes and steps at or beyond the training difficulty, with mean success rates of 0.850 for PAMORT and 0.675 for the baseline across eight tasks. These results demonstrate robust unsecured-payload transportation with online adaptation of the locomotion--payload trade-off from proprioceptive information.