CALM: Current Aligned Link Manipulation for Single Arm Oversized Object Lifting
Authors: Jun Hu, Sihan Chen, Kosta Jovanovic, David Navarro-Alarcon, Xueqian Wang, Jia Pan, Peng Zhou
Organizations: School of Advanced Engineering, Great Bay University, Dongguan, Guangdong, China · Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China · School of Electrical Engineering, University of Belgrade, Belgrade, Serbia · Department of Mechanical Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong SAR, China
Most robots manipulate objects solely with their end effectors, whereas humans flexibly leverage different body parts, such as the forearm and elbow, especially when handling oversized objects. Learning such whole-arm manipulation is chal-lenging due to long-horizon sparse rewards, limited contact sens-ing, and the sim-to-real gap in contact and actuator dynamics. To address these challenges, we propose Current-Aligned Link Manipulation, a framework for learning long-horizon contact-rich manipulation using motor current as joint load related feedback. Three stage-specific policies first learn repositioning, grasping, and lifting using privileged simulation information, and a stage router sequences them to generate complete task demonstrations. For sim-to-real transfer, a causal current mapper predicts physical motor current from simulated joint histories, aligning the actuator current observation between simulation and hardware. A unified student policy then learns from these demonstrations using only deployable sensor observations and is further refined with DAgger. The task policies are trained entirely in simulation, and the final student is deployed on hardware. Experiments demonstrate 76.2% (762/1000 trials) complete-task success in simulation and 73.3% success (22/30 trials) on the physical robot for sequential oversized-object lifting.
Figures & tables
Fig. 1: Motivation and task overview. (a) When an object’s width exceeds the functional grasp aperture of one hand, (b) a person can recruit the whole arm to support and carry it. Analogously, (c) when the object’s width exceeds the aperture of the robot’s end effector gripper, we investigate whether a single rigid manipulator can use intermediate arm links to (d) reposition the object, (e) establish an opposed grasp, and (f) lift it.
Fig. 2: Overview of CALM. A causal Transformer maps simulated joint histories to physical motor current, while three privileged teachers learn repositioning, grasping, and lifting. During complete task rollouts, privileged simulation state selects the active teacher to produce complete trajectories under domain randomization. These trajectories supervise one stage aware student, which is refined with DAgger and deployed with deployable observations only.
Reward: (1) contact at designated links; (2) centered opposed grasp; (3) stable hold. Penalty: (1) missing or invalid contact; (2) box motion; (3) grip loss; (4) table contact; (5) motion and force variation after latching.
Lift πL
Reward: (1) grasp confirmation and maintenance; (2) lift progress and height; (3) settling and holding. Penalty: (1) weak or lost grasp; (2) premature or stalled lifting; (3) lateral motion; (4) wrist slip; (5) large actions before confirmation.
TABLE II: Principal rewards and penalties specific to each stage.
Question summary
Experiments providing evidence
Current alignment
Secs. IV-B and IV-E : current prediction accuracy and 10 hardware trials without current alignment
Student distillation
Secs. IV-C and IV-D : controlled comparison of the full student policy and its ablated variants
Physical CALM
Sec. IV-E : 30 hardware trials of the final CALM policy and a current alignment ablation comprising 10 additional trials, with a new random box placement before each trial
TABLE III: Research questions and corresponding experiments.
Fig. 3: Experimental setups. (a) Isaac Lab simulation environment. (b) Physical robot setup, where two cameras detect ArUco markers attached to the box to track its position.
Fig. 4: Offline current alignment. (a) Raw simulated joint effort (right axes), recorded physical motor current, and current predictions (left axes) over the first eligible continuous 15 s interval. (b) MAE for each joint and the mean across joints over all 19,213 held out samples. The simulated joint effort baseline is converted to amperes using a separate linear scaling for each joint fitted on the training data.
Fig. 5: Teacher routing and teacher to student transfer in simulation. (a) Routed teacher stage progression. Each stage reports routed reach followed by independently evaluated teacher success, and each failure branch terminates at the first unreached stage. (b) Ablation of stage supervision and DAgger. Cells report unconditional reposition completion, conditional R→G and G→L success, and overall task success over 1,000 episodes per policy.
Fig. 6: Physical CALM evaluation under random initial box placements. From left to right, each row shows the initial state, box repositioning, opposed link grasping, and the lifting outcome. (a) and (b) Successful trials with the small box. (c) Successful trial with the large box. (d) Failed trial in which the large box is dropped after grasping.