{"total":15,"items":[{"citing_arxiv_id":"2606.18799","ref_index":1,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"A Theory-Guided Advanced Regulatory Control Synthesis for Cooling-Limited Exothermic Semi-Batch Reactors","primary_cat":"eess.SY","submitted_at":"2026-06-17T08:13:36+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"A new synthesis workflow converts finite-horizon minimum-time optimality into a cooling-demand VPC architecture and safety requirements into near-boundary tuning rules, matching nominal NMPC performance while showing zero temperature-limit violations under mismatch and faults where the NMPC benchmar","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17256","ref_index":56,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Contrastive Action-Image Pre-training for Visuomotor Control","primary_cat":"cs.RO","submitted_at":"2026-06-15T20:00:20+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"CAIP learns action-aligned visual representations via contrastive pre-training on human hand keypoints from egocentric video, outperforming DINOv2, SigLIP, MVP, and R3M with >30% gains on real dexterous manipulation tasks.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.17055","ref_index":76,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"T-Rex: Tactile-Reactive Dexterous Manipulation","primary_cat":"cs.RO","submitted_at":"2026-06-15T17:59:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"T-Rex introduces a large tactile dataset and MoT architecture that achieves over 30% higher success rates than baselines on 12 tasks requiring force control and deformable object handling.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2606.05247","ref_index":4,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"DiffSlack: Learning under Nonlinear Inequality Constraints via Learnable Slack Variables","primary_cat":"cs.LG","submitted_at":"2026-06-03T11:58:06+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"DiffSlack introduces learnable slack variables and a damped Gauss-Newton projection to create a differentiable layer that enforces hard nonlinear inequality constraints in neural network outputs.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.23354","ref_index":30,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Physics-informed sparse identification-based tube model predictive control for aerial vehicles","primary_cat":"eess.SY","submitted_at":"2026-05-22T08:20:53+00:00","verdict":null,"verdict_confidence":null,"novelty_score":null,"formal_verification":null,"one_line_summary":null,"context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.20392","ref_index":28,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"VBT-MPC: Vision-Based Tactile MPC for Contour Following","primary_cat":"cs.RO","submitted_at":"2026-05-19T18:40:45+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"VBT-MPC performs robotic contour following by running MPC directly in vision-based tactile contour feature space and is tested on varied geometries in simulation and real experiments.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.19562","ref_index":2,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Learning-Accelerated Optimization-based Trajectory Planning for Cooperative Aerial-Ground Handover Missions","primary_cat":"cs.RO","submitted_at":"2026-05-19T09:07:59+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"LSTM-based neural predictions accelerate centralized optimization for aerial-ground handover trajectories, reporting over 3x speedup and 100% success rate versus cold starts.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.12735","ref_index":98,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"The Unified Autonomy Stack: Toward a Blueprint for Generalizable Robot Autonomy","primary_cat":"cs.RO","submitted_at":"2026-05-12T20:39:35+00:00","verdict":"ACCEPT","verdict_confidence":"MODERATE","novelty_score":4.0,"formal_verification":"none","one_line_summary":"An open-sourced Unified Autonomy Stack fuses LiDAR, radar, vision and inertial data with sampling-based planning and control barrier functions to deliver resilient autonomy on aerial and ground robots in challenging real-world settings.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2605.08438","ref_index":22,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Generalized Global Self-Optimizing Control for Chemical Processes: Part II Objective-Guided Controlled Variable Learning Approach","primary_cat":"math.OC","submitted_at":"2026-05-08T20:00:22+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"OGCVL integrates symbolic and numerical techniques to learn effective nonlinear controlled variables for scalable self-optimizing control in chemical processes.","context_count":1,"top_context_role":"background","top_context_polarity":"background","context_text":"To broaden the scope of this idea and to overcome these limitations, our research aims to extend the notion of economic weighting to a wider operational range. We adopt the Lagrangian multipliers method to evade the impact of varying active constraints. The Lagrangian function for Eq. (1) can be represented as L(u,d) =J(u,d) + ngX i=1 νigi(u,d), i= 1,2, ..., n g (22) whereν i ≥0, i= 1,2, ..., n g is the Lagrangian multiplier, andng denotes the number of constrains. Theorem 8(Lagrange multiplier theorem24).Letf:R n →Rbe the objective function, g:R n →R c be the constraints function, both belonging toC1 (that is, having continuous first derivatives). Letx opt be an optimal solution to the following optimization problem"},{"citing_arxiv_id":"2605.06495","ref_index":50,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Global self-optimizing control of batch processes","primary_cat":"math.OC","submitted_at":"2026-05-07T16:15:48+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A vectorized reformulation of global self-optimizing control makes structural causality constraints linear for batch processes and enables a shortcut method that yields simple, repetitive combination matrices for near-optimal control, shown on a fed-batch reactor.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2604.22672","ref_index":31,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Iterative Model-Learning Scheme via Gaussian Processes for Nonlinear Model Predictive Control of (Semi-)Batch Processes","primary_cat":"cs.LG","submitted_at":"2026-04-24T15:49:10+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"An iterative GP-based NMPC learning scheme for batch processes achieves 83% tracking error reduction after 4 iterations and 17-fold product mass increase by iteration 8 in simulations, matching full-model NMPC performance.","context_count":1,"top_context_role":"method","top_context_polarity":"use_method","context_text":"problem is solved with the optimization tool CasADi [ 30] with IPOPT [ 28] as solver. To provide the starting dataset, we implement the PI control on the system for tracking. The PID control with derivative term leads to large oscillations in states and co n- trols, due to the high noise variance. Therefore, we imple- ment PI control for this system. Using the Ziegler-Nichols tuning rule [ 31], we tune the PI controller with the fol- lowing settings, /u1D458/u1D443 = 114 , /u1D458/u1D43C = 0 . 3, to perform the tracking task at /u1D447/u1D460/u1D452/u1D461. The PI controller is implemented to manipulate the cooling jacket inlet temperature, /u1D447/u1D457,/u1D456/u1D45B. The cooling jacket inlet temperature is chosen out of the three available control variables due to its linear response and"},{"citing_arxiv_id":"2604.19148","ref_index":37,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Multi-Step Gaussian Process Propagation for Adaptive Path Planning","primary_cat":"cs.RO","submitted_at":"2026-04-21T06:55:14+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":6.0,"formal_verification":"none","one_line_summary":"OLAhGP optimizes receding-horizon waypoints using the Gaussian process posterior over multiple steps to produce more informative paths for environmental monitoring than prior methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2602.02394","ref_index":56,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"On the Practical Implementation of a Sequential Quadratic Programming Algorithm for Nonconvex Sum-of-squares Problems","primary_cat":"math.OC","submitted_at":"2026-02-02T17:56:01+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"A filter line search SQP algorithm reduces iterations and computation time for nonconvex SOS programs compared to prior methods.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2511.05757","ref_index":3,"ref_count":1,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Zero-Shot Function Encoder-Based Differentiable Predictive Control","primary_cat":"eess.SY","submitted_at":"2025-11-07T23:02:55+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":4.0,"formal_verification":"none","one_line_summary":"A differentiable framework integrates function encoder-based neural ODEs with predictive control to enable zero-shot adaptation of explicit policies across families of nonlinear systems.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null},{"citing_arxiv_id":"2503.04929","ref_index":4,"ref_count":2,"confidence":0.88,"is_internal_anchor":false,"paper_title":"Neural Configuration-Space Barriers for Manipulation Planning and Control","primary_cat":"cs.RO","submitted_at":"2025-03-06T20:00:56+00:00","verdict":"UNVERDICTED","verdict_confidence":"LOW","novelty_score":5.0,"formal_verification":"none","one_line_summary":"Neural CDF barriers enable efficient planning and distributionally robust safe control for manipulators in cluttered dynamic environments using only point-cloud observations.","context_count":0,"top_context_role":null,"top_context_polarity":null,"context_text":null}],"limit":50,"offset":0}