a nɀjpT @s dZddlZddlZejdZddZgdZddeeDZ Gd d d Z Gd d d e Z Gd dde Z Gddde Z Gddde ZGddde ZGddde ZGddde ZGddde Zdde e e eeeeefDZddiZd#dd Zd!d"ZdS)$aThe incentive library. A task is what the robot is being paid to do. It owns four things: any scenery it needs, the goal information the robot is allowed to observe, the reward, and what counts as failing. Nothing here tells the robot how to move. Every task is expressed as a payoff. Note on goal observations: a task like `goto` has to tell the robot where the goal is, and that information has to come from somewhere real. Treat those channels as a localisation source on the robot (UWB beacon, vision, or wheel odometry with a known origin). They are listed in the UI as goal sensing so the distinction from "free knowledge of world state" stays visible. Base tilt, velocity and absolute pose remain reward-only. Ngf@cCs<|\}}}}td||||dd||||S)N)mathatan2)qwxyzr )/home/ghrups/robot-bench/trainer/tasks.py _quat_yaws r ) cmd_vxcmd_wzspin_wz spin_driftgoal_bxgoal_by goal_range step_dist cmd_heightcCsi|]\}}||qSr r ).0icr r r (rc@seZdZdZdZdZdZdZdZgZ gZ dZ gZ dddZ ed d Zd d Zd dZddZddZddZddZddZddZeddZeddZdS) TaskbaseZBaseN(@unioncCs ||_|p i|_d|_||_dS)N?)speccfg curriculumlayoutselfr"r#r%r r r __init__7s z Task.__init__cCs|jdkrttSt|jS)Nr )r%len GOAL_CHANNELSchannelsr'r r r obs_extra=szTask.obs_extracCsiS)z?Goal information this incentive is allowed to publish, by name.r r'envr r r channel_valuesAszTask.channel_valuescs6|||jdkrtn|j}tfdd|DS)Nr csg|]}t|dqS))floatget)rnvr r HrzTask.obs..)r0r%r*r+nparray)r'r/namesr r5r obsEs zTask.obscCsdSNr r'r/rngr r r resetKsz Task.resetcCsdSr<r r=r r r on_stepNsz Task.on_stepcCsdS)z@World position of the current goal, or None. Drawn by the bench.Nr r.r r r goal_markerQszTask.goal_markercCstdSr<)NotImplementedError)r'r/actionr r r rewardUsz Task.rewardcCsdSNFr r.r r r terminatedXszTask.terminatedcCsdSrEr r.r r r success[sz Task.successcCs|jj|j}|jj|j}|dd tt|d|dd tt||j d|dd tt||j ddS)Ntorquer1r action_rate joint_nominal)rHrIrJ) dataqpos leg_joint_idsactuator_forceact_idxr3r2r8sumZ prev_actionZ q_nominal)r/rCrZq_legtaur r r _posture_s $$z Task._posturecCs<|dd|dd|j|dd |j|jddS) Naliver!upright@height @r)rSrTrV)r3up_zrVh_target)r/rr r r _stay_upls z Task._stay_up)Nr )__name__ __module__ __qualname__namelabel description actuatorspose init_noise goal_sensingr+ episode_spropsr(propertyr-r0r;r?r@rArDrFrG staticmethodrRrZr r r r r+s2   rc@s@eZdZdZdZdZdZddddd d d d Zd dZddZ dS)StandZstandz Stand stillzHold station on the wheels without falling. Staying put is rewarded by position, not by stillness, because balancing IS constant small wheel motion.$@r!@rW?~jtX?{Gzt?333333?)rSrTrVanchorrHrIrJcCs|jdd|_dSNr)base_poscopyoriginr=r r r r?sz Stand.resetcCsvttj|jdd|j}|||j}|jdt |d d|d<| | |||jt | |fS)Nrrp?)r2r8linalgnormrrrtrZWrexpupdaterRrPvalues)r'r/rCdrifttermsr r r rDs  "z Stand.rewardN) r[r\r]r^r_r`rerxr?rDr r r r rius ric@sDeZdZdZdZdZdZdZdZdddddZ dddd dd d Z d S) BalanceaSStand, but with the legs bolted at the standing pose. The whole problem becomes "drive the wheels forward and back to stay under the body" - two numbers instead of six. There is no reason to make the robot search leg poses before it can even stay upright, and a 2-D problem trains in minutes rather than half an hour. balancezBalance on the wheelszmLegs locked straight, so the only thing to learn is rolling the wheels back and forth to stay under the body.rj)Z l_wheel_aZ r_wheel_ar1l_hipl_kneer_hipr_kneegQ?g{Gz?)jointZbase_zZ base_rollZ base_pitchZbase_yawZbase_velN) r[r\r]__doc__r^r_r`rerarbrcr r r r r~sr~c seZdZdZdZdZdZdgZdgZdZ ddddd Z d d dd d d dddZ d fdd Z ddZ ddZddZddZddZddZZS)!BobaNBalance while deliberately changing height. Squatting and rising moves the centre of mass vertically, which changes the pendulum under the controller while it is running. Learning this after plain balancing, and before shoves, is the natural order: it is the first time the legs and the wheels have to cooperate. bobzBob up and downzFollow a height command while staying upright. The legs change the pendulum the wheels are balancing, so the two have to work together.zcommanded body heightrrr1rr!rkg@rmrn)rSrTrVtrack_hrprHrIrJNr csnt||||jdd|_|jdd|_|jdd|_t|jdd|_t |j|j|j|_ dS) Nlowg ףp= ?highgGz? resample_s@gates) superr(r#r3rrrintZn_gatesr8linspacerr& __class__r r r(s z Bob.__init__cCs^||_|jdd|_|j|_t|jtt t |j|j|_ | ||j|_dSrq)r>rrrsrtrYmidr2rrr8argminabstarget_pickr_nextr=r r r r?s & z Bob.resetcsjjj}jt| |gfddjD}|sftjtt t jjg}fdd|D}tj |p|_ dS)Ncs8g|]0}dd|kr,ddkrnq|qS)rg& .>rr rg)nearr r r7rzBob._pick..cs(g|] }t|tdddkr|qS)rư>)rgetattrrr,r r r7r)rrr$rr8r9rr2rrrr>choicer)r'r/spanZallowedoptionsr )rr'r rs&z Bob._pickcCsttt|j|jSr<)rr8rrrrr,r r r gate_indexszBob.gate_indexcCs(|j|jkr$|||j|j|_dSr<)trrrr=r r r r@s  z Bob.on_stepcCsd|j|jdiS)Nrrj)rrr.r r r r0szBob.channel_valuescCs|j|j}ttj|jdd|j}|jd|jd|j d}|jdt |d d|d<|jdt |d d|d<| | |||jt||fS) NrrSrT)rSrTrg{Gzd?rpru)rVrr2r8rvrwrrrtrxrXrryrzrRrPr{)r'r/rCerrr|r}r r r rDs  ""z Bob.reward)Nr )r[r\r]rr^r_r`rdr+rerbrxr(r?rrr@r0rD __classcell__r r rr rs$ rc s~eZdZdZdZdZddgZddgZdZd d d d d ddddd Z d!fdd Z ddZ ddZ ddZ ddZdd ZZS)"DrivedrivezDrive to commandz~Follow a forward-speed and turn-rate command that changes every few seconds. This is the general-purpose locomotion incentive.zcommanded forward speedcommanded turn raterrrr!rUrWrl333333?rmrnro皙?) rSrTrVtrack_vxtrack_wzrHrIrJ yaw_driftNr csLt||||j}|dddg|_|dddg|_|dd|_dS) Nvx_rangeg333333rwz_rangegr!r@)rr(r#r3rrr)r'r"r#r%rrr r r( s zDrive.__init__cCs||_||j|_dSr<)r> _resamplerrr=r r r r?sz Drive.resetcCs6|j}t|jj|j||jj|j|g|_dSr<)r$r8r9r>uniformrrcmd)r'sr r r rs zDrive._resamplecCs&|j|jkr"||j|j|_dSr<)rrrrr=r r r r@s z Drive.on_stepcCs|jd|jddS)Nrr)rr)rr.r r r r0#szDrive.channel_valuescCs|j|j}}|||j}|jdt|d|jdd d|d<|jdt|d|jdd d|d<|jd t|dd|d<|| |||jt | |fS)Nrrrrurrr) vel_local gyro_truerZrxrryrr2rzrRrPr{)r'r/rCr6rr}r r r rD&s00 z Drive.reward)Nr )r[r\r]r^r_r`rdr+rerxr(r?rr@r0rDrr r rr rs rc @sZeZdZdZdZdZddgZddgZdZd d d d d ddddZ ddZ ddZ ddZ dS)SpinspinzSpin on the spotzTurn at a commanded rate while holding position. Rewards yaw rate and punishes translation, so it has to counter-rotate the wheels rather than drive in a circle.rzdistance from startrrrjr!rUrWrk333333?rmrnro)rSrTrVrrprHrIrJcCsp||_|jdddg\}}|dt||j}||dkrDdnd|_|jj |j ddd  |_ dS) Nrgrr!?rrZgt_posr) r>r#r3rrr$random target_wzrK sensordataZ_gtrsrt)r'r/r>Zrng_loZrng_himagr r r r?<s z Spin.resetcCs,|jdd|j}|jttj|dS)Nr)rr)rrrtrr2r8rvrw)r'r/dr r r r0CszSpin.channel_valuescCs|j}ttj|jdd|j}|||j}|jdt |d|j d d|d<|jdt |d d|d<| | |||jt||fS)Nrrr!rpg ףp= ?)rr2r8rvrwrrrtrZrxrryrrzrRrPr{)r'r/rCrr|r}r r r rDGs ,"z Spin.rewardN) r[r\r]r^r_r`rdr+rerxr?r0rDr r r r r0src seZdZdZdZdZddgZgdZdZdd d d d d dddd Z d$fdd Z ddZ ddZ ddZ ddZddZd d!Zd"d#ZZS)%GoToZgotoz Go to a pointzDrive to marker A, then marker B, then A again. Rewards closing the distance, with a bonus for arriving. Nothing says how to get there.zbearing to goal (sin, cos)z range to goalrrrg4@r!rlrWrgD@g?rmrnro) rSrTrVprogressarriveheadingrHrIrJNr cs4t||||jdd|_|jdd|_dS)Nradius皙? tolerancer)rr(r#r3rrr&rr r r(^sz GoTo.__init__c Cs||_|jdd|j}|ddtj}t|t||t |gt|t|tj|t |tjgg|_ d|_ d|_ | ||_dS)Ngffffff?g?rr)r>rr$rrpir8r9cossin waypointswp_ireached_dist prev_dist)r'r/r>rangr r r r?cs ,z GoTo.resetcCs|j|jt|jSr<)rrr)r,r r r _goalosz GoTo._goalcCs"ttj||jddSrq)r2r8rvrwrrrr.r r r rrsz GoTo._distcCs"|}t|dt|ddgS)Nrrr1)rr2)r'r/rr r r rAuszGoTo.goal_markercCs||jdd}t|j}t| |dt| |d}t| |dt| |d}t||}||d||dt|dddS)Nrrrrg@ror) rrrr Z base_quatrrrhypotmin)r'r/rZyawbxbyZrng_r r r r0ys ((  zGoTo.channel_valuescCs||}|||j}|jd|j||d<||_|jd||d|d<d|d<||jkr|jd|d<|jd7_|jd7_|||_|| |||jt | |fS)Nrrrr1rr) rrZrxrr0rrrrzrRrPr{)r'r/rCrr}r r r rDs   z GoTo.rewardcCs |jdkSrq)rr.r r r rGsz GoTo.success)Nr )r[r\r]r^r_r`rdr+rerxr(r?rrrAr0rDrGrr r rr rQs$  rc seZdZdZdZdZdgZdgZdZddd d d d d ddZ d fdd Z e ddZ ddZ ddZddZddZddZZS)!ClimbZclimbz Climb a stepzGet over a raised threshold and keep going. The step height rises with the curriculum, so it learns the easy lip before the hard one.zdistance to step edgerg.@r!rlrrjgI@rmrnr)rSrTrVrcrossrHrIrJNr csDt||||jdd|_|jdd|_|jdd|_dS)Nstep_xg? max_heightg ףp= ?depthr)rr(r#r3rmax_hrr&rr r r(szClimb.__init__cCs|jdd|jS)Nrug?)rr$r,r r r rVsz Climb.heightcCsD|j}ddd|jdd|dg|j|jdd|dggddgS) NstepZobstacleZboxrrr1)gzG?g?gp= ף?r!)r^kindtypesizeposrgba)rVrr)r'hr r r build_propss zClimb.build_propscCsd|_t|jd|_dS)NFr)crossedr2rrprev_xr=r r r r?sz Climb.resetcCs"dtd|jt|jddiS)Nrrrr)rrr2rrr.r r r r0szClimb.channel_valuescCst|jd}|||j}|jd||j|d<||_d|d<|jsp||j|jdkrpd|_|jd|d<|| |||jt | |fS)Nrrr1rrT) r2rrrZrxrrrrrzrRrPr{)r'r/rCrr}r r r rDsz Climb.rewardcCs|jSr<)rr.r r r rGsz Climb.success)Nr )r[r\r]r^r_r`rdr+rerxr(rgrVrr?r0rDrGrr r rr rs"   rcsZeZdZdZdZdZdZdddddd d d Zdfdd ZddZ ddZ ddZ Z S)RecoverrecoverzSurvive shoveszStand while being pushed hard at random intervals. Trains the recovery reflex specifically, rather than hoping it falls out of the other incentives.rrlrkrWrmrnro)rSrTrVsettlerHrIrJNr cs8t||||jdd|_|jdddg|_dS)Nevery_srUimpulseg?g@)rr(r#r3rrr&rr r r(szRecover.__init__cCs||_|j|_dSr<)r>rrr=r r r r?sz Recover.resetcCs|j|jkr|j\}}||||||j}|ddtj}|jjd|t |7<|jjd|t |7<|j|j |_dS)Nrrr) rrrrr$rrrKqvelrrr)r'r/r>lohirrr r r r@s  zRecover.on_stepcCsl|j}|||j}|jdt|dd|dd d|d<|||||jt||fS)Nrrrrr) rrZrxrryrzrRrPr{)r'r/rCr6r}r r r rDs 2zRecover.reward)Nr ) r[r\r]r^r_r`rerxr(r?r@rDrr r rr rs rcCsi|] }|j|qSr )r^rrr r r rrZbalance_and_driverr cCspt|p d|pd}|tvr4td|dttt|dipDi|i}||pZit||||dS)Nrrzunknown incentive z; have tasks)r%)ALIASESr3REGISTRYKeyErrorsorteddictrz)r"r^r#r%Ztask_cfgr r r make_tasks rcCsddtDS)z7Machine-readable list for the bench's incentive picker.c Ss\g|]T}|j|j|j|jt|jt|jt|j t |d|j rPt|j ndd qS)rN) r^r_r`rerdr+Z reward_termsZ has_sceneryra) r^r_r`striprelistrdr+rrxkeyshasattrrarr r r r7s  zcatalogue..)rr{r r r r catalogues r)NNr )rrnumpyr8rZDEGr r* enumerateZ GOAL_INDEXrrir~rrrrrrrrrrr r r r s&  JM2!I6$