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Robotics Scenarios

Physical-machine scenarios, grouped by sensing, control, motion, and learning.

All domains · Classes A–Z

Source locations

  • Inputs and calculations: mlsysim/engine/scenario_definitions/robotics/.
  • Named configurations: mlsysim/engine/scenario_catalog/robotics/.
  • Result schemas: mlsysim/engine/result_types/robotics.py.

Public imports and existing class reference URLs remain stable.

Scenario contracts

Contact

  • ForceTailScenario: A contact force over trials trials with a benign mean_force and a p99_force tail, against site’s tripwire and structural limit.
  • HeadingDriftScenario: A persistent heading_offset at machine’s drive limit in site’s aisle: the angular error an open-loop replay scores, and where the closed loop spends the side clearance (y = v sin(eps) t).
  • StiffContactScenario: A stiff contact of stiffness (a linear spring, F = k x): the force a residual penetration commands, and the penetrations at site’s latch tripwire and limit.

Control

  • CycleBudgetScenario: Stage sub-budgets of one permission check against site’s declared WCET and deadline.
  • GravityBiasedJointStopScenario: One loaded arm joint at joint_speed braking with machine’s peak torque while gravity holds bias_fraction of that peak along (descending) or against (ascending) the motion.
  • HoldIntervalDriftScenario: Drift while a drive holds the last command for one period: travel at speed plus the half acceleration period^2 an independent acceleration bound allows.
  • TorqueHalfSpaceProjectionScenario: A torque proposal against one barrier half-space normal . u <= bound: the least-change projection (calc_cbf_qp_orthogonal_projection) against a per-axis clamp.

Demonstrations

  • ContactGridScenario: A contact task binned by contact angle and normal force, and the cells each archive occupies.
  • CorrelatedLogScenario: duration of records logged at rate whose adjacent records have lag-one correlation lag1_correlation: how few independent samples the record count holds.
  • DemonstrationCampaignScenario: Scheduled sessions of attempt-and-reset cycles: demonstrations kept per session, the campaign that keeps target_kept of them, and the wear it puts on a gear and a cable.
  • GripperTransferScenario: A normalized gripper command from a donor gripper replayed on a target with a shorter stroke and a lower continuous force rating, against a part of part_width.
  • ImitationBoundScenario: Worst-case imitation cost over one episode at control_rate with per-step error per_step_error: open loop, behavioral cloning, and aggregation with recoverability u.
  • PairingLagScenario: An observation paired with an action lag later, against a clearance, at a transit speed and at site’s guarded and fast latch approaches.
  • ScenarioTableCoverageScenario: A scenario table of offsets by speeds joined against an archive demonstrated within logged_offset at demonstrated_speed, plus recovery collections at added_offsets.
  • StateGridScenario: A state space of state_components axes cut into bins per axis, against the records fleet robots log at log_rate for duration.
  • TeleopIngestionScenario: Raw telemetry of one teleoperated session on machine: its navigation and wrist cameras, external_cameras RGB-D cameras, and the kinematic record at the permission tick.
  • TeleopLoopScenario: One teleoperated correction: display, human, command transport, and mechanical response.

Diagnostics

Estimation

  • BeliefExpiryScenario: A belief with initial error, growth law, and task tolerance; optionally admitted at use_age under age_bound.
  • ClearanceQueryScenario: Sphere-hull clearance queries on site.machine’s permission controller, and a snapshot of the stopping corridor.
  • EncoderRateScenario: An encoder of flop_per_frame on every site.machine navigation frame, against the host SoC’s dense FP16 peak.
  • EpochGapScenario: Named acquisition epochs in one cycle; the pair to fuse is projected across its gap at speed.
  • EstimatorConsistencyScenario: A qualification campaign of runs scalar estimates, and single-update gates against fusion’s misdated update.
  • FreeSpaceExpiryScenario: site’s rack end: free space ahead (clear distance) and abreast (side clearance) eroding at the walking speed.
  • FrozenTransformScenario: site’s conveyor object tracked by a camera lost at loss_time; its last transform republished every republish_period.
  • HostMapSizingScenario: A workcell of extent mapped at pitch: dense grid against a hashed band of band_half_width around surface_area.
  • InertialCoastScenario: site.machine’s IMU coasting duration without a fix, seen at the rack-end distance.
  • OdometryDriftScenario: A square-path odometry benchmark’s return errors, priced against site’s localization bound at its aisle speed and length.
  • RangeFusionScenario: The range to site’s rack-end upright, predicted over one lidar sweep and updated by the lidar, then stereo; the stereo update misdated by age’s arrival stamp at speed.
  • RaymarchTrafficScenario: One depth stream ray-marched into a map on site.machine’s host SoC.
  • ReplayBufferScenario: A rewind buffer at site.machine’s IMU rate reaching back one observation age.
  • SensorBiasDriftScenario: A steady reading whose sensor bias drifts at drift_rate for span, while the estimator reports reported_sigma.
  • SkewedRecordsScenario: Two records skew apart, compared at site: the base at its drive limit and a person at the walking speed close on each other.
  • StaleTransformScenario: belief’s cached camera edge composed with the current wrist pose, one site.machine wrist-camera frame old.

Grounding

  • ConfidenceRegionScenario: A Gaussian position estimate with principal standard deviations sigmas: its coverage region against site’s grasp tolerance.
  • DynamicReachScenario: A target target_distance from the gripper at cadence.machine’s TCP speed ceiling, against the cadence scenario’s evidence windows.
  • ForwardOccupancyScenario: A coworker’s forward occupancy set at cadence.site’s design approach speed, screened over the cadence scenario’s evidence windows and over a forecast horizon, against the aisle.
  • GoalSetMembershipScenario: Terminal TCP offsets along one axis tested against a goal set whose semi-axes all equal site’s grasp tolerance: the normalized ellipsoidal distance, inside when at most one.
  • IntentCadenceScenario: site’s conveyor target proposed by machine’s intent model: evidence horizons against the model’s cadence, a host stall of stall, and the stop onset after expiry.
  • IntentLeaseExpiryScenario: The intent lease for the cadence scenario’s target: its evidence horizons, an administrative_lease, and the stopping deadline from clearance at the TCP speed ceiling, with the arm’s credible deceleration from its handbook worst-case stop.
  • ReferentSetScenario: Raw per-box scores of retained candidates screened by a conformal threshold calibrated on calibration_count scenes at miss rate alpha, whose calibration score is threshold_score.

Learning

  • ContactDeadlineScenario: A constant-speed drive into site’s latch plate: when the force ramp k v t reaches the tripwire and the limit, when braking can begin, and the time left, at both approaches.
  • DiscountHorizonScenario: One rollout of decisions step apart under discount discount: how far the return reaches, in decisions and in seconds, and the discount that keeps retained_weight of the final reward at the first decision.
  • ForceRiseGapScenario: An illustrative linear drive regulating target_force against a surface, in simulation and on hardware, with one stated discrepancy in each reality-gap channel: dynamics (force-rise rate and drive delay), timing (p99 actuation delay against the simulator’s), sensing (range bias and dropout), and appearance (pixel difference hiding a contact-patch shift).
  • IterativeDecoderBudgetScenario: machine’s one-pass chunk decoder against an iterative head that encodes once and runs denoising_steps dependent evaluations in denoising_latency, and a flow_steps head at the same cost per evaluation, on the setpoint step and the renewal period with renewal_jitter held free.
  • OnRobotFineTuneScenario: interaction_steps setpoints step apart collected on the machine to absorb a plate_offset, of which exceed_fraction cross the contact tripwire.
  • ParallelSimRunScenario: A GPU-parallel on-policy run: robots environments each stepping steps_per_update times per update for updates updates within training_time, at policy_rate.
  • SoftContactExploitScenario: machine’s arm coasting into site’s latch plate at the fast approach: the peak force a penalty spring that lets the gripper sink penalty_penetration reports, the peak the real plate imposes on a rigid arm, and the arm stiffness at which the series chain reaches the limit. The moving mass is the arm’s effective contact mass without its payload capacity (the gripper holds no object at the door).
  • TrialCampaignScenario: trials hardware trials of rollout each, decisions decision_step apart, with automated and manual resets, on one machine and on a farm; what a demonstration-seeded seeded_budget allows; and the same trials in simulator at its published throughput.

Perception

Placement

  • RailDroopScenario: A current step delta_i in rise_time through a package’s inductance and resistance, and the alpha-power gate-delay stretch at the drooped rail.
  • SharedDramReadScenario: site’s permission tick placed on the machine’s application SoC, its snapshot read queued behind backlogs on the shared DRAM controller.
  • StallShareScenario: site’s unloaded permission tick with a share of it slowed slowdown times.
  • ThermalThrottleScenario: site’s permission tick on a cluster whose clock a thermal governor halves.
  • TickTraceScenario: Percentiles of a permission tick’s execution time over a trace of ticks periods, cold and warm, against site’s declared WCET and deadline.

Planning

  • ChunkReplacementScenario: Replacing machine’s admitted chunk at site: capture-to-admission latency against the renewal period and the chunk lease, with an interference_tail on the inference pass.
  • DynamicWindowScenario: machine’s base over one chunk renewal at site: its dynamic window and the classical admissible speed against the site’s clear distance and credible deceleration.
  • HandoffExposureScenario: A replanner at replan_rate over mission_time whose handoffs are each late with late_probability, held to mission_budget for the whole mission.
  • JerkLimitedStopScenario: A machine at speed that stops along an S-curve at deceleration after delay.
  • SeamBridgeScenario: A velocity_gap at a chunk seam on joint, closed in one tick or by a quintic bridge over bridge_duration, against machine’s arm torque-rate limit. loaded_inertia (optional) prices the one-tick step with the payload’s inertia added.
  • ShoulderSwingScenario: machine’s shoulder holding its rated payload at full planar reach, with joint’s reflected rotor inertia, swinging swing rest to rest inside its datasheet limits.
  • SpillLimitedStopScenario: The free_stop scenario’s machine carrying an open container of inner_radius filled freeboard below the rim: the deceleration the liquid allows, that stop, and the carry speed.
  • StaleAnchorScenario: A joint of machine’s arm turning at joint_speed while a replacement takes delay, anchored at the pose sampled at the request; the lever arm is the planar reach.
  • TrajectoryDeadlinesScenario: A trajectory record for machine’s arm at site: a replacement requested at request_delay with a latency_target and schedule_slack, a blend deadline, the commit instant when stop (a JerkLimitedStopScenario) must begin, and a late_wait.
  • TwoLinkJointRateScenario: machine’s arm as a planar two-link chain at elbow_angle: the joint rates its TCP speed limit asks in the weakest direction, exact and with damped least squares at damping.

Plant

Proposers

  • DecoderFamiliesScenario: One fresh machine.chunk_horizon-step chunk from backbone, per decoder family.
  • EvidenceHorizonScenario: site’s conveyor target tracked by machine’s chunk policy: evidence horizon against renewal.
  • KvHistoryScenario: Key-value cache for history_window of retained context at history_rate beside machine’s weights.
  • ProposerTrafficScenario: machine’s intent model and chunk policy sharing its memory bus (weight traffic only).
  • RateTargetScenario: evaluation held against a target_rate: one fresh evaluation per period, not pipelined.
  • VlaEvaluationScenario: One fresh evaluation of machine’s intent model on its application processor.
  • VlaTokenBudgetScenario: Tokens a VLA backbone reads from views square camera frames, and their memory per token.
  • WeightOnlyQuantizationScenario: One backbone priced at several weight formats against measurements of the same formats.

Realtime

  • CanFdBusScenario: num_axes servo axes on one CAN FD bus, frames_per_axis frames each per cycle.
  • EtherCatCycleScenario: num_axes drives on one EtherCAT chain, bytes_per_axis of process data each per cycle.
  • LeaseBudgetScenario: site.machine’s chunk lease at speed: its stopping-budget cost and the lease rule’s bounds.
  • PhaseLagBudgetScenario: A loop with crossover crossover sampled every sample_period, with compute and transport delays.
  • ResponseTimeScenario: site’s permission check as a periodic task: R = C + B + I against its deadline.

Site Limits

Stopping

  • SpeedStepScenario: Raising the speed setpoint from base to raised (two stopping budgets at one deceleration).
  • StoppingBudgetScenario: A machine at speed that must stop: travel during delay, then braking at deceleration.
  • TractionLossScenario: Floor friction falls from mu_before to mu_after under a machine that assumes deceleration.
  • TravelDuringLatencyScenario: Distance a vehicle at speed covers during latency; optionally a remote-path delay range.

Supervision

  • BrakingBlendScenario: A bumpless transfer into braking on a site’s base at speed, and an arm joint’s blend.
  • CommandAuthenticationScenario: Remote commands authenticated on a machine’s permission processor, priced against a takeover budget.
  • DecisionChainScenario: links independently kept decision records behind one admitted action, each retrievable with probability p; the whole chain resolves with p^links for each p in link_probabilities.
  • DetectionShortfallScenario: site.machine at speed with a stop sized for a group detected in reference of frames, met by groups detected less often; misses allowed in every frame with probability all_miss_probability, frames independent.
  • EventRetentionScenario: Event-triggered retention: pre_trigger before and post_trigger after each of events_per_day events, against keeping the whole operating_day.
  • ExposureConcentrationScenario: encounter_share of a site’s machine-person encounters fall on user_share of its people: their per-person exposure against the site average, and everyone else’s.
  • OversightTimeScenario: A person asked to act through budget (a TakeoverBudgetScenario) against the time the hazard at its site leaves: the clear distance at the request speed.
  • SubgroupSampleScenario: An evaluation set of total people with a group of small_share, both detected at recall: exact intervals for each group, and the people per group a two-sided test at significance with power needs to tell rate_a from rate_b.
  • SupervisorQueueScenario: One remote assistant serving machines, each asking for help at request_rate, each request holding the assistant for service_time; utilization_cap sizes the largest fleet.
  • TakeoverBudgetScenario: A machine at speed at a site asks a person to take over within takeover.
  • TakeoverCeilingLadderScenario: One takeover budget per sourced human response, each plus the same console transport.
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